Task-Agnostic Adaptation for Safe Human-Robot Handover

TL;DR

This paper introduces a task-agnostic adaptable controller for human-robot handover that adjusts to environmental and human variability, ensuring safety and transferability across different robot platforms.

Contribution

The paper presents a novel task-agnostic control framework that adapts to lighting, human behavior, and robot platform differences in human-robot interaction tasks.

Findings

Achieves consistent performance across different environmental conditions.

Ensures safe interaction with diverse human behaviors.

Enables transferability between different robot platforms.

Abstract

Human-robot interaction (HRI) is an important component to improve the flexibility of modern production lines. However, in real-world applications, the task (\ie the conditions that the robot needs to operate on, such as the environmental lighting condition, the human subjects to interact with, and the hardware platforms) may vary and it remains challenging to optimally and efficiently configure and adapt the robotic system under these changing tasks. To address the challenge, this paper proposes a task-agnostic adaptable controller that can 1) adapt to different lighting conditions, 2) adapt to individual behaviors and ensure safety when interacting with different humans, and 3) enable easy transfer across robot platforms with different control interfaces. The proposed framework is tested on a human-robot handover task using the FANUC LR Mate 200id/7L robot and the Kinova Gen3 robot. Experiments show that the proposed task-agnostic controller can achieve consistent performance across different tasks.