Shared Control of Robot-Robot Collaborative Lifting with Agent Postural and Force Ergonomic Optimization
This work addresses the challenge of improving collaborative capabilities in humanoid robots for multi-agent scenarios, though it appears incremental as it builds on existing ergonomic optimization methods.
The paper tackles the problem of enabling efficient robot-robot collaboration in lifting tasks by developing a shared control framework that optimizes posture and contact forces for ergonomics, validated experimentally with two iCub humanoid robots performing payload lifting sequences.
Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking into account the ergonomic requirements of the robots through the optimization of posture and contact forces. The framework is validated in an experimental scenario with two iCub humanoid robots performing different payload lifting sequences.