@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},

number = {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 = {Authorship - Corresponding, JCR Q1, Jour - Biomimetics (Biomimetics)},

pubstate = {published},

tppubtype = {article}

}

@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},

number = {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 = {Authorship - Corresponding, JCR Q1, Jour - Biomimetics (Biomimetics)},

pubstate = {published},

tppubtype = {article}

}