Compliant Explicit Reference Governor for Contact Friendly Robotic Manipulators

This paper introduces the Compliant Explicit Reference Governor (C-ERG), an extension of the Explicit Reference Governor that allows the robot to operate safely while in contact with the environment. The C-ERG is an intermediate layer that can be placed between a high-level planner and a low-level controller: its role is to enforce operational constraints and to enable the smooth transition between free-motion and contact operations. The C-ERG ensures safety by limiting the total energy available to the robotic arm at the time of contact. In the absence of contact, however, the C-ERG does not penalize the system performance. Numerical examples showcase the behavior of the C-ERG for increasingly complex systems.

Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.