Co-active Learning to Adapt Humanoid Movement for Manipulation

TL;DR

This paper introduces a co-active learning framework enabling humanoid robots to adapt manipulation movements to new environments and constraints through human-in-the-loop interactions, improving generalization over traditional methods.

Contribution

It presents a novel co-active learning approach that adapts imitation trajectories considering user feedback and environmental constraints for humanoid manipulation tasks.

Findings

Effective adaptation of robot movements to new constraints

Improved generalization over traditional motion primitives

Validated on a humanoid platform with positive results

Abstract

In this paper we address the problem of robot movement adaptation under various environmental constraints interactively. Motion primitives are generally adopted to generate target motion from demonstrations. However, their generalization capability is weak while facing novel environments. Additionally, traditional motion generation methods do not consider the versatile constraints from various users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt robot end-effector's movement for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with various constraints. The framework also considers user's feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. The implemented system generalizes trained motion primitives to various situations with different constraints considering user preferences. Experiments on a humanoid platform validate the effectiveness of our approach.