Bridging Locomotion and Manipulation Using Reconfigurable Robotic Limbs via Reinforcement Learning.

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

SUBMITTER: Sun H 

PROVIDER: S-EPMC10452096 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature