Journal Articles
2024
21.
Ning Guo; Xudong Han; Shuqiao Zhong; Zhiyuan Zhou; Jian Lin; Jiansheng Dai; Fang Wan; Chaoyang Song
Proprioceptive State Estimation for Amphibious Tactile Sensing Journal Article Forthcoming
In: IEEE Transactions on Robotics, Forthcoming, (Accepted for Special Issue on Tactile Robotics).
@article{Guo2024ProprioceptiveState,
title = {Proprioceptive State Estimation for Amphibious Tactile Sensing},
author = {Ning Guo and Xudong Han and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Jiansheng Dai and Fang Wan and Chaoyang Song},
doi = {10.48550/arXiv.2312.09863},
year = {2024},
date = {2024-09-04},
urldate = {2024-09-04},
journal = {IEEE Transactions on Robotics},
abstract = {This paper presents a novel vision-based proprioception approach for a soft robotic finger that can estimate and reconstruct tactile interactions in both terrestrial and aquatic environments. The key to this system lies in the finger's unique metamaterial structure, which facilitates omni-directional passive adaptation during grasping, protecting delicate objects across diverse scenarios. A compact in-finger camera captures high-framerate images of the finger's deformation during contact, extracting crucial tactile data in real-time. We present a volumetric discretized model of the soft finger and use the geometry constraints captured by the camera to find the optimal estimation of the deformed shape. The approach is benchmarked using a motion capture system with sparse markers and a haptic device with dense measurements. Both results show state-of-the-art accuracies, with a median error of 1.96 mm for overall body deformation, corresponding to 2.1% of the finger's length. More importantly, the state estimation is robust in both on-land and underwater environments as we demonstrate its usage for underwater object shape sensing. This combination of passive adaptation and real-time tactile sensing paves the way for amphibious robotic grasping applications.},
note = {Accepted for Special Issue on Tactile Robotics},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
This paper presents a novel vision-based proprioception approach for a soft robotic finger that can estimate and reconstruct tactile interactions in both terrestrial and aquatic environments. The key to this system lies in the finger's unique metamaterial structure, which facilitates omni-directional passive adaptation during grasping, protecting delicate objects across diverse scenarios. A compact in-finger camera captures high-framerate images of the finger's deformation during contact, extracting crucial tactile data in real-time. We present a volumetric discretized model of the soft finger and use the geometry constraints captured by the camera to find the optimal estimation of the deformed shape. The approach is benchmarked using a motion capture system with sparse markers and a haptic device with dense measurements. Both results show state-of-the-art accuracies, with a median error of 1.96 mm for overall body deformation, corresponding to 2.1% of the finger's length. More importantly, the state estimation is robust in both on-land and underwater environments as we demonstrate its usage for underwater object shape sensing. This combination of passive adaptation and real-time tactile sensing paves the way for amphibious robotic grasping applications.
20.
Xudong Han; Ning Guo; Yu Jie; He Wang; Fang Wan; Chaoyang Song
On Flange-Based 3D Hand-Eye Calibration for Soft Robotic Tactile Welding Journal Article
In: Measurement, vol. 238, iss. 10, pp. 115376, 2024.
@article{Han2024OnFlange,
title = {On Flange-Based 3D Hand-Eye Calibration for Soft Robotic Tactile Welding},
author = {Xudong Han and Ning Guo and Yu Jie and He Wang and Fang Wan and Chaoyang Song},
doi = {10.1016/j.measurement.2024.115376},
year = {2024},
date = {2024-07-26},
journal = {Measurement},
volume = {238},
issue = {10},
pages = {115376},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
19.
Ning Guo; Xudong Han; Shuqiao Zhong; Zhiyuan Zhou; Jian Lin; Fang Wan; Chaoyang Song
Reconstructing Soft Robotic Touch via In-Finger Vision Journal Article
In: Advanced Intelligent Systems, iss. 10, pp. 2400022, 2024, (Selected as the Front Cover of the October 2024 Issue).
@article{Guo2024ReconstructingSoft,
title = {Reconstructing Soft Robotic Touch via In-Finger Vision},
author = {Ning Guo and Xudong Han and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Fang Wan and Chaoyang Song},
doi = {10.1002/aisy.202400022},
year = {2024},
date = {2024-07-17},
urldate = {2024-07-17},
journal = {Advanced Intelligent Systems},
issue = {10},
pages = {2400022},
abstract = {Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive R2 scores of 0.9681, 0.9415, and 0.9541 along the x, y, and z axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.},
note = {Selected as the Front Cover of the October 2024 Issue},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive R2 scores of 0.9681, 0.9415, and 0.9541 along the x, y, and z axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.
18.
Xiaobo Liu; Xudong Han; Wei Hong; Fang Wan; Chaoyang Song
Proprioceptive Learning with Soft Polyhedral Networks Journal Article
In: The International Journal of Robotics Research, 2024, (OnlineFirst).
@article{Liu20242024ProprioceptiveLearning,
title = {Proprioceptive Learning with Soft Polyhedral Networks},
author = {Xiaobo Liu and Xudong Han and Wei Hong and Fang Wan and Chaoyang Song},
doi = {10.1177/02783649241238765},
year = {2024},
date = {2024-03-13},
urldate = {2024-03-13},
journal = {The International Journal of Robotics Research},
abstract = {Proprioception is the “sixth sense” that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at low costs in mechanical design and algorithmic computation. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion-tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low material cost with more than one million use cycles for tasks such as sensitive and competitive grasping and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.},
note = {OnlineFirst},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Proprioception is the “sixth sense” that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at low costs in mechanical design and algorithmic computation. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion-tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low material cost with more than one million use cycles for tasks such as sensitive and competitive grasping and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.
17.
Tianyu Wu; Yujian Dong; Xiaobo Liu; Xudong Han; Yang Xiao; Jinqi Wei; Fang Wan; Chaoyang Song
Vision-based Tactile Intelligence with Soft Robotic Metamaterial Journal Article
In: Materials & Design, vol. 238, iss. 2, pp. 112629, 2024.
@article{Wu2024VisionBasedb,
title = {Vision-based Tactile Intelligence with Soft Robotic Metamaterial},
author = {Tianyu Wu and Yujian Dong and Xiaobo Liu and Xudong Han and Yang Xiao and Jinqi Wei and Fang Wan and Chaoyang Song},
doi = {10.1016/j.matdes.2024.112629},
year = {2024},
date = {2024-01-11},
journal = {Materials & Design},
volume = {238},
issue = {2},
pages = {112629},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
16.
Yu Pan; Xuanyi Dai; Fang Wan; Chaoyang Song; James KH Tsoi; Edmond HN Pow
A Novel Post-Processing Strategy to Improve the Accuracy of Complete-Arch Intraoral Scanning for Implants: An In Vitro Study Journal Article
In: Journal of Dentistry, vol. 139, iss. 12, pp. 104761, 2023.
@article{Pan2023ANovel,
title = {A Novel Post-Processing Strategy to Improve the Accuracy of Complete-Arch Intraoral Scanning for Implants: An In Vitro Study},
author = {Yu Pan and Xuanyi Dai and Fang Wan and Chaoyang Song and James KH Tsoi and Edmond HN Pow},
doi = {10.1016/j.jdent.2023.104761},
year = {2023},
date = {2023-10-23},
journal = {Journal of Dentistry},
volume = {139},
issue = {12},
pages = {104761},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
15.
Ning Guo; Xudong Han; Xiaobo Liu; Shuqiao Zhong; Zhiyuan Zhou; Jian Lin; Jiansheng Dai; Fang Wan; Chaoyang Song
Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater Journal Article
In: Advanced Intelligent Systems, vol. 6, iss. 1, pp. 2300382, 2023, (Selected as the Front Cover of the January 2024 Issue).
@article{Guo2024AutoencodingA,
title = {Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater},
author = {Ning Guo and Xudong Han and Xiaobo Liu and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Jiansheng Dai and Fang Wan and Chaoyang Song},
doi = {10.1002/aisy.202300382},
year = {2023},
date = {2023-10-22},
urldate = {2023-10-22},
journal = {Advanced Intelligent Systems},
volume = {6},
issue = {1},
pages = {2300382},
abstract = {Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a supervised variational autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learns a series of latent representations of the soft mechanics transferable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.},
note = {Selected as the Front Cover of the January 2024 Issue},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a supervised variational autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learns a series of latent representations of the soft mechanics transferable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.
14.
Xiaobo Liu; Xudong Han; Ning Guo; Fang Wan; Chaoyang Song
Bio-inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception Journal Article
In: Biomimetics, vol. 8, iss. 6, pp. 501, 2023.
@article{Liu2023BioInspired,
title = {Bio-inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception},
author = {Xiaobo Liu and Xudong Han and Ning Guo and Fang Wan and Chaoyang Song},
doi = {10.3390/biomimetics8060501},
year = {2023},
date = {2023-10-21},
urldate = {2023-10-21},
journal = {Biomimetics},
volume = {8},
issue = {6},
pages = {501},
abstract = {In-hand object pose estimation is challenging for humans and robots due to occlusion caused by the hand and object. This paper proposes a soft finger that integrates inner vision with kinesthetic sensing to estimate object pose inspired by human fingers. The soft finger has a flexible skeleton and skin that adapts to different objects, and the skeleton deformations during interaction provide contact information obtained by the image from the inner camera. The proposed framework is an end-to-end method that uses raw images from soft fingers to estimate in-hand object pose. It consists of an encoder for kinesthetic information processing and an object pose and category estimator. The framework was tested on seven objects, achieving an impressive error of 2.02 mm and 11.34 degrees for pose error and 99.05% for classification.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In-hand object pose estimation is challenging for humans and robots due to occlusion caused by the hand and object. This paper proposes a soft finger that integrates inner vision with kinesthetic sensing to estimate object pose inspired by human fingers. The soft finger has a flexible skeleton and skin that adapts to different objects, and the skeleton deformations during interaction provide contact information obtained by the image from the inner camera. The proposed framework is an end-to-end method that uses raw images from soft fingers to estimate in-hand object pose. It consists of an encoder for kinesthetic information processing and an object pose and category estimator. The framework was tested on seven objects, achieving an impressive error of 2.02 mm and 11.34 degrees for pose error and 99.05% for classification.
13.
Yuping Gu; Ziqian Wang; Shihao Feng; Haoran Sun; Haibo Lu; Jia Pan; Fang Wan; Chaoyang Song
Computational Design Towards Energy Efficient Optimization in Overconstrained Robotic Limbs Journal Article
In: Journal of Computational Design and Engineering, vol. 10, iss. 5, pp. 1941–1956, 2023.
@article{Gu2023ComputationalDesign,
title = {Computational Design Towards Energy Efficient Optimization in Overconstrained Robotic Limbs},
author = {Yuping Gu and Ziqian Wang and Shihao Feng and Haoran Sun and Haibo Lu and Jia Pan and Fang Wan and Chaoyang Song},
doi = {10.1093/jcde/qwad083},
year = {2023},
date = {2023-08-22},
urldate = {2023-08-22},
journal = {Journal of Computational Design and Engineering},
volume = {10},
issue = {5},
pages = {1941–1956},
abstract = {Legged robots are constantly evolving, and energy efficiency is a major driving factor in their design. However, combining mechanism efficiency and trajectory planning can be challenging. This work proposes a computational optimization framework for optimizing leg design during basic walking while maximizing energy efficiency. We generalize the robotic limb design as a four-bar linkage-based design pool and optimize the leg using an evolutionary algorithm. The leg configuration and design parameters are optimized based on user-defined objective functions. Our framework was validated by comparing it to measured data on our prototype quadruped robot for forward trotting. The Bennett robotic leg was advantageous for omni-directional locomotion with enhanced energy efficiency.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Legged robots are constantly evolving, and energy efficiency is a major driving factor in their design. However, combining mechanism efficiency and trajectory planning can be challenging. This work proposes a computational optimization framework for optimizing leg design during basic walking while maximizing energy efficiency. We generalize the robotic limb design as a four-bar linkage-based design pool and optimize the leg using an evolutionary algorithm. The leg configuration and design parameters are optimized based on user-defined objective functions. Our framework was validated by comparing it to measured data on our prototype quadruped robot for forward trotting. The Bennett robotic leg was advantageous for omni-directional locomotion with enhanced energy efficiency.
12.
Jiayu Huo; Jingran Wang; Yuqin Guo; Wanghongjie Qiu; Mingdong Chen; Harry Asada; Fang Wan; Chaoyang Song
Reconfigurable Design and Modeling of an Underwater Superlimb for Diving Assistance Journal Article
In: Advanced Intelligent Systems, vol. 5, iss. 11, pp. 2300245, 2023.
@article{Huo1012ReconfigurableDesign,
title = {Reconfigurable Design and Modeling of an Underwater Superlimb for Diving Assistance},
author = {Jiayu Huo and Jingran Wang and Yuqin Guo and Wanghongjie Qiu and Mingdong Chen and Harry Asada and Fang Wan and Chaoyang Song},
doi = {10.1002/aisy.202300245},
year = {2023},
date = {2023-08-17},
urldate = {2023-08-17},
journal = {Advanced Intelligent Systems},
volume = {5},
issue = {11},
pages = {2300245},
abstract = {This study presents the design of an underwater superlimb as a wearable robot, providing divers with mobility assistance and freeing their hands for manipulating tools underwater. The wearable design features a thrust vectoring system with two 3D-printed, waterproofed modules. The module with adjustable connections and strapping holes is designed to enable reconfiguration for multiple purposes, including regular use as an underwater superlimb for divers, manually operated as a handheld glider for swimmers, combined with an amphibian, legged robot as a quadruped superlimb, and coupled as a dual-unit autonomous underwater vehicle for underwater navigation. The kinematics and dynamics of the prototype and all of its reconfigured modes are developed. A sliding-mode controller is also introduced to achieve stable simulation in PyBullet. Field tests further support the feasibility of the underwater superlimb when worn on a test diver in a swimming pool. As the first underwater superlimb presented in the literature, this study opens new doors for supernumerary robotic limbs in underwater scenarios with multifunctional reconfiguration.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study presents the design of an underwater superlimb as a wearable robot, providing divers with mobility assistance and freeing their hands for manipulating tools underwater. The wearable design features a thrust vectoring system with two 3D-printed, waterproofed modules. The module with adjustable connections and strapping holes is designed to enable reconfiguration for multiple purposes, including regular use as an underwater superlimb for divers, manually operated as a handheld glider for swimmers, combined with an amphibian, legged robot as a quadruped superlimb, and coupled as a dual-unit autonomous underwater vehicle for underwater navigation. The kinematics and dynamics of the prototype and all of its reconfigured modes are developed. A sliding-mode controller is also introduced to achieve stable simulation in PyBullet. Field tests further support the feasibility of the underwater superlimb when worn on a test diver in a swimming pool. As the first underwater superlimb presented in the literature, this study opens new doors for supernumerary robotic limbs in underwater scenarios with multifunctional reconfiguration.
11.
Haoran Sun; Linhan Yang; Yuping Gu; Jia Pan; Fang Wan; Chaoyang Song
Bridging Locomotion and Manipulation Using Reconfigurable Robotic Limbs via Reinforcement Learning Journal Article
In: Biomimetics, vol. 8, iss. 4, pp. 364, 2023.
@article{Sun2023BridgingLocomotion,
title = {Bridging Locomotion and Manipulation Using Reconfigurable Robotic Limbs via Reinforcement Learning},
author = {Haoran Sun and Linhan Yang and Yuping Gu and Jia Pan and Fang Wan and Chaoyang Song},
doi = {10.3390/biomimetics8040364},
year = {2023},
date = {2023-08-14},
urldate = {2023-08-14},
journal = {Biomimetics},
volume = {8},
issue = {4},
pages = {364},
abstract = {Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of research remains to identify the data-driven evidence for further research. This paper explores a unified formulation of the loco-manipulation problem using reinforcement learning (RL) by reconfiguring robotic limbs with an overconstrained design into multi-legged and multi-fingered robots. Such design reconfiguration allows for adopting a co-training architecture for reinforcement learning towards a unified loco-manipulation policy. As a result, we find data-driven evidence to support the transferability between locomotion and manipulation skills using a single RL policy with a multilayer perceptron or graph neural network. We also demonstrate the Sim2Real transfer of the learned loco-manipulation skills in a robotic prototype. This work expands the knowledge frontiers on loco-manipulation transferability with learning-based evidence applied in a novel platform with overconstrained robotic limbs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of research remains to identify the data-driven evidence for further research. This paper explores a unified formulation of the loco-manipulation problem using reinforcement learning (RL) by reconfiguring robotic limbs with an overconstrained design into multi-legged and multi-fingered robots. Such design reconfiguration allows for adopting a co-training architecture for reinforcement learning towards a unified loco-manipulation policy. As a result, we find data-driven evidence to support the transferability between locomotion and manipulation skills using a single RL policy with a multilayer perceptron or graph neural network. We also demonstrate the Sim2Real transfer of the learned loco-manipulation skills in a robotic prototype. This work expands the knowledge frontiers on loco-manipulation transferability with learning-based evidence applied in a novel platform with overconstrained robotic limbs.
2022
10.
Yuping Gu; Shihao Feng; Yuqin Guo; Fang Wan; Jiansheng Dai; Jia Pan; Chaoyang Song
Overconstrained Coaxial Design of Robotic Legs with Omni-directional Locomotion Journal Article
In: Mechanism and Machine Theory, vol. 176, iss. 10, pp. 105018, 2022.
@article{Gu2022OverconstrainedCoaxial,
title = {Overconstrained Coaxial Design of Robotic Legs with Omni-directional Locomotion},
author = {Yuping Gu and Shihao Feng and Yuqin Guo and Fang Wan and Jiansheng Dai and Jia Pan and Chaoyang Song},
doi = {10.1016/j.mechmachtheory.2022.105018},
year = {2022},
date = {2022-07-18},
urldate = {2022-07-18},
journal = {Mechanism and Machine Theory},
volume = {176},
issue = {10},
pages = {105018},
abstract = {While being extensively researched in literature, overconstrained linkages’ engineering potential is yet to be explored. This study investigates the design of overconstrained linkages as robotic legs with coaxial actuation starting with the simplest case, Bennett linkage, to establish the theoretical foundations and engineering advantages of a class of overconstrained robots. We proposed a parametric design of the spatial links and joints in alternative forms so that one can fabricate these overconstrained limbs via 3D printing and then attach the linkage coaxially to a pair of servo actuators as a reconfigurable leg module. We adopted multi-objective optimization to refine the design parameters by analyzing its manipulability metric and force transmission, enabling omni-directional ground locomotion projected from a three-dimensional surface workspace. The proposed prototype quadruped was capable of omni-directional locomotion and had a minimal turning radius (0.2 Body Length) using the fewest actuators. We further explored the kinematics and design potentials to generalize the proposed method for all overconstrained 5R and 6R linkages, paving the path for a future direction in overconstrained robotics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
While being extensively researched in literature, overconstrained linkages’ engineering potential is yet to be explored. This study investigates the design of overconstrained linkages as robotic legs with coaxial actuation starting with the simplest case, Bennett linkage, to establish the theoretical foundations and engineering advantages of a class of overconstrained robots. We proposed a parametric design of the spatial links and joints in alternative forms so that one can fabricate these overconstrained limbs via 3D printing and then attach the linkage coaxially to a pair of servo actuators as a reconfigurable leg module. We adopted multi-objective optimization to refine the design parameters by analyzing its manipulability metric and force transmission, enabling omni-directional ground locomotion projected from a three-dimensional surface workspace. The proposed prototype quadruped was capable of omni-directional locomotion and had a minimal turning radius (0.2 Body Length) using the fewest actuators. We further explored the kinematics and design potentials to generalize the proposed method for all overconstrained 5R and 6R linkages, paving the path for a future direction in overconstrained robotics.
9.
Haokun Wang; Xiaobo Liu; Nuofan Qiu; Ning Guo; Fang Wan; Chaoyang Song
DeepClaw 2.0: A Data Collection Platform for Learning Human Manipulation Journal Article
In: Frontiers in Robotics and AI, vol. 9, pp. 787291, 2022.
@article{Wang2022DeepClaw2.0,
title = {DeepClaw 2.0: A Data Collection Platform for Learning Human Manipulation},
author = {Haokun Wang and Xiaobo Liu and Nuofan Qiu and Ning Guo and Fang Wan and Chaoyang Song},
doi = {10.3389/frobt.2022.787291},
year = {2022},
date = {2022-03-15},
journal = {Frontiers in Robotics and AI},
volume = {9},
pages = {787291},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021
8.
Haiyang Jiang; Xudong Han; Yonglin Jing; Ning Guo; Fang Wan; Chaoyang Song
Rigid-Soft Interactive Design of a Lobster-Inspired Finger Surface for Enhanced Grasping Underwater Journal Article
In: Frontiers in Robotics and AI, vol. 8, pp. 787187, 2021.
@article{Jiang2021RigidSoft,
title = {Rigid-Soft Interactive Design of a Lobster-Inspired Finger Surface for Enhanced Grasping Underwater},
author = {Haiyang Jiang and Xudong Han and Yonglin Jing and Ning Guo and Fang Wan and Chaoyang Song},
doi = {10.3389/frobt.2021.787187},
year = {2021},
date = {2021-12-22},
journal = {Frontiers in Robotics and AI},
volume = {8},
pages = {787187},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
7.
Baiyue Wang; Weijie Guo; Shihao Feng; Hongdong Yi; Fang Wan; Chaoyang Song
Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing Journal Article
In: IEEE Robotics and Automation Letters, vol. 6, iss. 3, pp. 5284-5291, 2021.
@article{Wang2021VolumetricallyEnhanced,
title = {Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing},
author = {Baiyue Wang and Weijie Guo and Shihao Feng and Hongdong Yi and Fang Wan and Chaoyang Song},
doi = {10.1109/LRA.2021.3072859},
year = {2021},
date = {2021-04-13},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
issue = {3},
pages = {5284-5291},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
6.
Linhan Yang; Xudong Han; Weijie Guo; Fang Wan; Jia Pan; Chaoyang Song
Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping Journal Article
In: IEEE Robotics and Automation Letters, vol. 6, iss. 2, pp. 3817-3824, 2021.
@article{Yang2021LearningBasedb,
title = {Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping},
author = {Linhan Yang and Xudong Han and Weijie Guo and Fang Wan and Jia Pan and Chaoyang Song},
doi = {10.1109/LRA.2021.3065186},
year = {2021},
date = {2021-03-11},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
issue = {2},
pages = {3817-3824},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
5.
Fang Wan; Chaoyang Song
Flange-Based Hand-Eye Calibration Using a 3D Camera with High Resolution, Accuracy, and Frame Rate Journal Article
In: Frontiers in Robotics and AI, vol. 7, pp. 65, 2020.
@article{Wan2020FlangeBased,
title = {Flange-Based Hand-Eye Calibration Using a 3D Camera with High Resolution, Accuracy, and Frame Rate},
author = {Fang Wan and Chaoyang Song},
doi = {10.3389/frobt.2020.00065},
year = {2020},
date = {2020-05-29},
journal = {Frontiers in Robotics and AI},
volume = {7},
pages = {65},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
4.
Fang Wan; Haokun Wang; Jiyuan Wu; Yujia Liu; Sheng Ge; Chaoyang Song
A Reconfigurable Design for Omni-adaptive Grasp Learning Journal Article
In: IEEE Robotics and Automation Letters, vol. 5, iss. 3, pp. 4210-4217, 2020.
@article{Wan2020AReconfigurable,
title = {A Reconfigurable Design for Omni-adaptive Grasp Learning},
author = {Fang Wan and Haokun Wang and Jiyuan Wu and Yujia Liu and Sheng Ge and Chaoyang Song},
doi = {10.1109/lra.2020.2982059},
year = {2020},
date = {2020-03-19},
journal = {IEEE Robotics and Automation Letters},
volume = {5},
issue = {3},
pages = {4210-4217},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3.
Linhan Yang; Fang Wan; Haokun Wang; Xiaobo Liu; Yujia Liu; Jia Pan; Chaoyang Song
Rigid-Soft Interactive Learning for Robust Grasping Journal Article
In: IEEE Robotics and Automation Letters, vol. 5, iss. 2, pp. 1720-1727, 2020.
@article{Yang2020RigidSoftb,
title = {Rigid-Soft Interactive Learning for Robust Grasping},
author = {Linhan Yang and Fang Wan and Haokun Wang and Xiaobo Liu and Yujia Liu and Jia Pan and Chaoyang Song},
doi = {10.1109/lra.2020.2969932},
year = {2020},
date = {2020-01-28},
journal = {IEEE Robotics and Automation Letters},
volume = {5},
issue = {2},
pages = {1720-1727},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2018
2.
Fang Wan; Chaoyang Song
A Neural Network with Logical Reasoning based on Auxiliary Inputs Journal Article
In: Frontiers in Robotics and AI, vol. 5, pp. 86, 2018.
@article{Wan2018ANeural,
title = {A Neural Network with Logical Reasoning based on Auxiliary Inputs},
author = {Fang Wan and Chaoyang Song},
doi = {10.3389/frobt.2018.00086},
year = {2018},
date = {2018-07-30},
journal = {Frontiers in Robotics and AI},
volume = {5},
pages = {86},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2017
1.
Yaohui Chen; Fang Wan; Tong Wu; Chaoyang Song
Soft-Rigid Interaction Mechanism towards a Lobster-inspired Hybrid Actuator Journal Article
In: Journal of Micromechanics and Microengineering, vol. 28, iss. 1, pp. 14007, 2017.
@article{Chen2017SoftRigid,
title = {Soft-Rigid Interaction Mechanism towards a Lobster-inspired Hybrid Actuator},
author = {Yaohui Chen and Fang Wan and Tong Wu and Chaoyang Song},
doi = {10.1088/1361-6439/aa9e25},
year = {2017},
date = {2017-12-15},
journal = {Journal of Micromechanics and Microengineering},
volume = {28},
issue = {1},
pages = {14007},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Conference Papers
2024
21.
Linhan Yang; Lei Yang; Haoran Sun; Zeqing Zhang; Haibin He; Fang Wan; Chaoyang Song; Jia Pan
One Fling to Goal: Environment-aware Dynamics for Goal-conditioned Fabric Flinging Conference Forthcoming
Workshop on the Algorithmic Foundations of Robotics (WAFR), Chicago, USA, Forthcoming, (Accepted).
@conference{Yang2024OneFling,
title = {One Fling to Goal: Environment-aware Dynamics for Goal-conditioned Fabric Flinging},
author = {Linhan Yang and Lei Yang and Haoran Sun and Zeqing Zhang and Haibin He and Fang Wan and Chaoyang Song and Jia Pan},
doi = {10.48550/arXiv.2406.14136},
year = {2024},
date = {2024-07-09},
booktitle = {Workshop on the Algorithmic Foundations of Robotics (WAFR)},
address = {Chicago, USA},
note = {Accepted},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
20.
Yenan Chen; Chuye Zhang; Pengxi Gu; Jianuo Qiu; Jiayi Yin; Nuofan Qiu; Guojing Huang; Bangchao Huang; Zishang Zhang; Hui Deng; Wei Zhang; Fang Wan; Chaoyang Song
IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR2024), Chicago, USA, 2024.
@conference{Chen2024EvolutionaryMorphology,
title = {Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning},
author = {Yenan Chen and Chuye Zhang and Pengxi Gu and Jianuo Qiu and Jiayi Yin and Nuofan Qiu and Guojing Huang and Bangchao Huang and Zishang Zhang and Hui Deng and Wei Zhang and Fang Wan and Chaoyang Song},
doi = {10.1109/ReMAR61031.2024.10618090},
year = {2024},
date = {2024-06-23},
booktitle = {IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR2024)},
address = {Chicago, USA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
19.
Sen Li; Fang Wan; Chaoyang Song
Active Surface with Passive Omni-Directional Adaptation for In-Hand Manipulation Conference
IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR2024), Chicago, USA, 2024.
@conference{Li2024ActiveSurface,
title = {Active Surface with Passive Omni-Directional Adaptation for In-Hand Manipulation},
author = {Sen Li and Fang Wan and Chaoyang Song},
doi = {10.1109/ReMAR61031.2024.10619925},
year = {2024},
date = {2024-06-23},
urldate = {2024-06-23},
booktitle = {IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR2024)},
address = {Chicago, USA},
abstract = {Soft fingers with omni-directional adaptability ex-cel in 3D twisting, outperforming two-dimensional self-adaptive hands using a finger rotation mechanism to achieve similar adaptability. In this study, we present the design of a soft robotic finger with an active surface on an omni-adaptive structure, which can be easily installed on existing grippers and achieve stability and dexterity for in-hand manipulation. The system's active surfaces initially transfer the object from the fingertip segment with less compliance to the middle segment of the finger with superior adaptability. Despite the omni-directional deformation of the finger, in-hand manipulation can still be executed with controlled active surfaces. We characterized the soft finger's stiffness distribution and simplified models to assess the feasibility of lifting and reorienting a grasped object in a 3D twisting state. A set of experiments on in-hand manipulation was performed with the proposed fingers, demonstrating the dexterity and robustness of the strategy.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Soft fingers with omni-directional adaptability ex-cel in 3D twisting, outperforming two-dimensional self-adaptive hands using a finger rotation mechanism to achieve similar adaptability. In this study, we present the design of a soft robotic finger with an active surface on an omni-adaptive structure, which can be easily installed on existing grippers and achieve stability and dexterity for in-hand manipulation. The system's active surfaces initially transfer the object from the fingertip segment with less compliance to the middle segment of the finger with superior adaptability. Despite the omni-directional deformation of the finger, in-hand manipulation can still be executed with controlled active surfaces. We characterized the soft finger's stiffness distribution and simplified models to assess the feasibility of lifting and reorienting a grasped object in a 3D twisting state. A set of experiments on in-hand manipulation was performed with the proposed fingers, demonstrating the dexterity and robustness of the strategy.
18.
Nuofan Qiu; Fang Wan; Chaoyang Song
Describing Robots from Design to Learning: Towards an Interactive Lifecycle Representation of Robots Conference Forthcoming
IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024), Tokyo, Japan, Forthcoming, (Accepted).
@conference{Qiu2024DescribingRobots,
title = {Describing Robots from Design to Learning: Towards an Interactive Lifecycle Representation of Robots},
author = {Nuofan Qiu and Fang Wan and Chaoyang Song},
doi = {10.48550/arXiv.2312.12295},
year = {2024},
date = {2024-06-08},
booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024)},
address = {Tokyo, Japan},
note = {Accepted},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
17.
Tianyu Wu; Yujian Dong; Yang Xiao; Jinqi Wei; Fang Wan; Chaoyang Song
Vision-based, Low-cost, Soft Robotic Tongs for Shareable and Reproducible Tactile Learning Conference Forthcoming
IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024), Tokyo, Japan, Forthcoming, (Accepted).
@conference{Wu2024VisionBased,
title = {Vision-based, Low-cost, Soft Robotic Tongs for Shareable and Reproducible Tactile Learning},
author = {Tianyu Wu and Yujian Dong and Yang Xiao and Jinqi Wei and Fang Wan and Chaoyang Song},
year = {2024},
date = {2024-06-08},
booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024)},
address = {Tokyo, Japan},
note = {Accepted},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
2023
16.
Xudong Han; Sheng Liu; Fang Wan; Chaoyang Song
Vision-based Tactile Sensing for an Omni-adaptive Soft Finger. Conference
IEEE International Conference on Development and Learning (ICDL), Macau SAR, 2023.
@conference{Han2023VisionBased,
title = {Vision-based Tactile Sensing for an Omni-adaptive Soft Finger.},
author = {Xudong Han and Sheng Liu and Fang Wan and Chaoyang Song},
doi = {10.1109/ICDL55364.2023.10364455},
year = {2023},
date = {2023-11-09},
booktitle = {IEEE International Conference on Development and Learning (ICDL)},
address = {Macau SAR},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
15.
Xiaobo Liu; Fang Wan; Sheng Ge; Haokun Wang; Haoran Sun; Chaoyang Song
Jigsaw-based Benchmarking for Learning Robotic Manipulation Conference
IEEE International Conference on Advanced Robotics and Mechatronics (ICARM), Sanya, China, 2023.
@conference{Liu2023JigsawBased,
title = {Jigsaw-based Benchmarking for Learning Robotic Manipulation},
author = {Xiaobo Liu and Fang Wan and Sheng Ge and Haokun Wang and Haoran Sun and Chaoyang Song},
doi = {10.1109/ICARM58088.2023.10218784},
year = {2023},
date = {2023-07-08},
booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)},
address = {Sanya, China},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
14.
Yuqin Guo; Rongzheng Zhang; Wanghongjie Qiu; Harry Asada; Fang Wan; Chaoyang Song
IEEE International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand, 2023.
@conference{Guo2023UnderwaterIntention,
title = {Underwater Intention Recognition using Head Motion and Throat Vibration for Supernumerary Robotic Assistance},
author = {Yuqin Guo and Rongzheng Zhang and Wanghongjie Qiu and Harry Asada and Fang Wan and Chaoyang Song},
doi = {10.1109/CASE56687.2023.10260480},
year = {2023},
date = {2023-06-26},
booktitle = {IEEE International Conference on Automation Science and Engineering (CASE)},
address = {Auckland, New Zealand},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2021
13.
Fang Wan; Xiaobo Liu; Ning Guo; Xudong Han; Feng Tian; Chaoyang Song
Visual Learning Towards Soft Robot Force Control using a 3D Metamaterial with Differential Stiffness Conference Forthcoming
Conference on Robot Learning (CoRL), London & Virtual, Forthcoming.
@conference{Wan2022VisualLearning,
title = {Visual Learning Towards Soft Robot Force Control using a 3D Metamaterial with Differential Stiffness},
author = {Fang Wan and Xiaobo Liu and Ning Guo and Xudong Han and Feng Tian and Chaoyang Song},
year = {2021},
date = {2021-11-08},
booktitle = {Conference on Robot Learning (CoRL)},
address = {London & Virtual},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
12.
Shihao Feng; Yuping Gu; Weijie Guo; Yuqin Guo; Fang Wan; Jia Pan; Chaoyang Song
An Overconstrained Robotic Leg with Coaxial Quasi-direct Drives for Omni-directional Ground Mobility Conference
IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 2021.
@conference{Feng2021AnOverconstrained,
title = {An Overconstrained Robotic Leg with Coaxial Quasi-direct Drives for Omni-directional Ground Mobility},
author = {Shihao Feng and Yuping Gu and Weijie Guo and Yuqin Guo and Fang Wan and Jia Pan and Chaoyang Song},
doi = {10.1109/ICRA48506.2021.9561829},
year = {2021},
date = {2021-05-30},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
address = {Xi’an, China},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
11.
Linhan Yang; Xudong Han; Weijie Guo; Fang Wan; Jia Pan; Chaoyang Song
Learning-based Optoelectronically Innervated Tactile Finger for Rigid- Soft Interactive Grasping Conference
IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 2021, (Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3065186).
@conference{Yang2021LearningBased,
title = {Learning-based Optoelectronically Innervated Tactile Finger for Rigid- Soft Interactive Grasping},
author = {Linhan Yang and Xudong Han and Weijie Guo and Fang Wan and Jia Pan and Chaoyang Song},
year = {2021},
date = {2021-05-30},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
address = {Xi’an, China},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3065186},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
10.
Weijie Guo; Baiyue Wang; Shihao Feng; Hongdong Yi; Fang Wan; Chaoyang Song
Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing Conference
IEEE International Conference on Soft Robotics (RoboSoft), New Haven, CT, USA, 2021, (Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3072859).
@conference{Guo2021VolumetricallyEnhanced,
title = {Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing},
author = {Weijie Guo and Baiyue Wang and Shihao Feng and Hongdong Yi and Fang Wan and Chaoyang Song},
year = {2021},
date = {2021-04-12},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft)},
address = {New Haven, CT, USA},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3072859},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
9.
Haiyang Jiang; Yonglin Jing; Ning Guo; Weijie Guo; Fang Wan; Chaoyang Song
Lobster-inspired Finger Surface Design for Grasping with Enhanced Robustness Conference
IEEE International Conference on Soft Robotics (RoboSoft), New Haven, CT, USA, 2021.
@conference{Jiang2021LobsterInspired,
title = {Lobster-inspired Finger Surface Design for Grasping with Enhanced Robustness},
author = {Haiyang Jiang and Yonglin Jing and Ning Guo and Weijie Guo and Fang Wan and Chaoyang Song},
doi = {10.1109/RoboSoft51838.2021.9479215},
year = {2021},
date = {2021-04-12},
urldate = {2021-04-12},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft)},
address = {New Haven, CT, USA},
abstract = {This paper presents a lobster-inspired design of a soft finger's contact surface for grasping with enhanced robustness. The lobsters, while living on the seabed with sediments of various sizes, sources, materials, and life forms, exhibit exceptional capabilities in object manipulation under-water using angled-claws with two fingers. By inspecting the geometric features of the lobster tooth, we proposed a series of finger surface designs molded with silicone. We tested the surface friction using traditional methods to shortlist our design pool, and further verified their performance using robotic arm grasping against a series of challenging objects from the EGAD, the Evolved Grasping Analysis Dataset. Results show that, in certain cases, the lobster-inspired finger surface design yields an enhanced grasping success rate by 56% at most than those without the surface. Furthermore, we propose a minimum setup for robotic grasping using NVidia Jetson Xavier, Intel RealSense D435, and the proposed soft gripper to be compatible with most robotic manipulators as a cost-effective configuration for shareable and reproducible research.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This paper presents a lobster-inspired design of a soft finger's contact surface for grasping with enhanced robustness. The lobsters, while living on the seabed with sediments of various sizes, sources, materials, and life forms, exhibit exceptional capabilities in object manipulation under-water using angled-claws with two fingers. By inspecting the geometric features of the lobster tooth, we proposed a series of finger surface designs molded with silicone. We tested the surface friction using traditional methods to shortlist our design pool, and further verified their performance using robotic arm grasping against a series of challenging objects from the EGAD, the Evolved Grasping Analysis Dataset. Results show that, in certain cases, the lobster-inspired finger surface design yields an enhanced grasping success rate by 56% at most than those without the surface. Furthermore, we propose a minimum setup for robotic grasping using NVidia Jetson Xavier, Intel RealSense D435, and the proposed soft gripper to be compatible with most robotic manipulators as a cost-effective configuration for shareable and reproducible research.
2020
8.
Fang Wan; Haokun Wang; Xiaobo Liu; Linhan Yang; Chaoyang Song
DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation Conference
IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA, 2020.
@conference{Wan2020DeepClaw1.0,
title = {DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation},
author = {Fang Wan and Haokun Wang and Xiaobo Liu and Linhan Yang and Chaoyang Song},
doi = {10.1109/aim43001.2020.9159011},
year = {2020},
date = {2020-07-06},
booktitle = {IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)},
address = {Boston, MA, USA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
7.
Linhan Yang; Fang Wan; Haokun Wang; Xiaobo Liu; Yujia Liu; Jia Pan; Chaoyang Song
Rigid-Soft Interactive Learning for Robust Grasping Conference
IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, (Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2969932).
@conference{Yang2020RigidSoft,
title = {Rigid-Soft Interactive Learning for Robust Grasping},
author = {Linhan Yang and Fang Wan and Haokun Wang and Xiaobo Liu and Yujia Liu and Jia Pan and Chaoyang Song},
year = {2020},
date = {2020-05-31},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
address = {Paris, France},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2969932},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
6.
Zeyi Yang; Sheng Ge; Fang Wan; Yujia Liu; Chaoyang Song
Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger Conference
IEEE International Conference on Soft Robotics (RoboSoft), New Haven, CT, USA, 2020.
@conference{Yang2020ScalableTactile,
title = {Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger},
author = {Zeyi Yang and Sheng Ge and Fang Wan and Yujia Liu and Chaoyang Song},
doi = {10.1109/robosoft48309.2020.9116026},
year = {2020},
date = {2020-05-15},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft)},
address = {New Haven, CT, USA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
5.
Xia Wu; Haiyuan Liu; Ziqi Liu; Mingdong Chen; Fang Wan; Chenglong Fu; Harry Asada; Zheng Wang; Chaoyang Song
Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance Conference
IEEE International Conference on Soft Robotics (RoboSoft), New Haven, CT, USA, 2020.
@conference{Wu2020RoboticCane,
title = {Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance},
author = {Xia Wu and Haiyuan Liu and Ziqi Liu and Mingdong Chen and Fang Wan and Chenglong Fu and Harry Asada and Zheng Wang and Chaoyang Song},
doi = {10.1109/robosoft48309.2020.9116028},
year = {2020},
date = {2020-05-15},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft)},
address = {New Haven, CT, USA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
4.
Fang Wan; Haokun Wang; Jiyuan Wu; Yujia Liu; Sheng Ge; Chaoyang Song
Reconfigurable Design for Omni-adaptive Grasp Learning Conference
IEEE International Conference on Soft Robotics (RoboSoft), New Haven, CT, USA, 2020, (Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2982059).
@conference{Wan2020ReconfigurableDesign,
title = {Reconfigurable Design for Omni-adaptive Grasp Learning},
author = {Fang Wan and Haokun Wang and Jiyuan Wu and Yujia Liu and Sheng Ge and Chaoyang Song},
year = {2020},
date = {2020-05-15},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft)},
address = {New Haven, CT, USA},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2982059},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2017
3.
Fang Wan; Zheng Wang; Brooke Franchuk; Xinyao Hu; Zhenglong Sun; Chaoyang Song
Hybrid Actuator Design for a Gait Augmentation Wearable Conference
IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, 2017.
@conference{Wan2017HybridActuator,
title = {Hybrid Actuator Design for a Gait Augmentation Wearable},
author = {Fang Wan and Zheng Wang and Brooke Franchuk and Xinyao Hu and Zhenglong Sun and Chaoyang Song},
doi = {10.1109/robio.2017.8324761},
year = {2017},
date = {2017-12-05},
booktitle = {IEEE International Conference on Robotics and Biomimetics (ROBIO)},
address = {Macau},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2.
Yaohui Chen; Sing Le; Qiao Chu Tan; Oscar Lau; Fang Wan; Chaoyang Song
A Reconfigurable Hybrid Actuator with Rigid and Soft Components Conference
IEEE International Conference on Robotics and Automation (ICRA), Marina Bay Sands, Singapore, 2017.
@conference{Chen2017AReconfigurable,
title = {A Reconfigurable Hybrid Actuator with Rigid and Soft Components},
author = {Yaohui Chen and Sing Le and Qiao Chu Tan and Oscar Lau and Fang Wan and Chaoyang Song},
doi = {10.1109/icra.2017.7988691},
year = {2017},
date = {2017-05-29},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
address = {Marina Bay Sands, Singapore},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
1.
Yaohui Chen; Sing Le; Qiao Chu Tan; Oscar Lau; Fang Wan; Chaoyang Song
A Lobster-inspired Robotic Glove for Hand Rehabilitation Conference
IEEE International Conference on Robotics and Automation (ICRA), Marina Bay Sands, Singapore, 2017.
@conference{Chen2017ALobsterICRA,
title = {A Lobster-inspired Robotic Glove for Hand Rehabilitation},
author = {Yaohui Chen and Sing Le and Qiao Chu Tan and Oscar Lau and Fang Wan and Chaoyang Song},
doi = {10.1109/icra.2017.7989556},
year = {2017},
date = {2017-05-29},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
address = {Marina Bay Sands, Singapore},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}