Learning Coordinated Tasks using Reinforcement Learning in Humanoids

TL;DR

This paper introduces a reinforcement learning framework for humanoid robots to learn coordinated, collision-free tasks in cluttered environments, emphasizing motion planning and trajectory smoothing for improved real-time performance.

Contribution

It presents a novel multi-task reinforcement learning approach using DiGrad for humanoids, incorporating trajectory smoothing to enhance coordination and collision avoidance.

Findings

Humanoids successfully planned collision-free trajectories in real-time.

Articulated torso improves coordination between arms during tasks.

Framework effectively handles varying environment complexities.

Abstract

With the advent of artificial intelligence and machine learning, humanoid robots are made to learn a variety of skills which humans possess. One of fundamental skills which humans use in day-to-day activities is performing tasks with coordination between both the hands. In case of humanoids, learning such skills require optimal motion planning which includes avoiding collisions with the surroundings. In this paper, we propose a framework to learn coordinated tasks in cluttered environments based on DiGrad - A multi-task reinforcement learning algorithm for continuous action-spaces. Further, we propose an algorithm to smooth the joint space trajectories obtained by the proposed framework in order to reduce the noise instilled during training. The proposed framework was tested on a 27 degrees of freedom (DoF) humanoid with articulated torso for performing coordinated object-reaching task with both the hands in four different environments with varying levels of difficulty. It is observed that the humanoid is able to plan collision free trajectory in real-time. Simulation results also reveal the usefulness of the articulated torso for performing tasks which require coordination between both the arms.