A Supervisory Learning Control Framework for Autonomous & Real-time Task Planning for an Underactuated Cooperative Robotic task

TL;DR

This paper presents a deep reinforcement learning-based supervisory control framework for cooperative manipulation by underactuated robots, enabling real-time task planning, robustness, and high success rates in pick-and-place tasks.

Contribution

It introduces a novel offline-trained supervisory policy for multi-agent manipulation, integrating task and motion planning with real-time adaptability and zero-shot real-world deployment.

Findings

Achieved over 90% success rate in real-world experiments.

Enabled real-time replanning for robustness against failures.

Demonstrated effective coordination of underactuated manipulators.

Abstract

We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to reposition an object within their shared work space. Control of multi-agent systems for manipulation tasks cannot rely on individual control strategies with little to no communication between the agents that serve the common objective through swarming. Instead a coordination strategy is required that queries subtasks to the individual agents. We formulate the problem in a Task And Motion Planning (TAMP) setting, while considering a decomposition strategy that allows us to treat the task and motion planning problems separately. We solve the supervisory planning problem offline using deep Reinforcement Learning techniques resulting into a supervisory policy capable of coordinating the two manipulators into a successful execution of the pick-and-place task. Additionally, a benefit of solving the task planning problem offline is the possibility of real-time (re)planning, demonstrating robustness in the event of subtask execution failure or on-the-fly task changes. The framework achieved zero-shot deployment on the real setup with a success rate that is higher than 90%.