Anticipatory Human-Robot Collaboration via Multi-Objective Trajectory Optimization

TL;DR

This paper introduces CoMOTO, a multi-objective trajectory optimization framework that predicts human motion to enhance safety, comfort, and efficiency in human-robot collaboration.

Contribution

The paper presents a novel trajectory optimization method that integrates stochastic human motion prediction with multi-objective cost functions for improved collaboration.

Findings

Outperforms existing methods in safety metrics

Enhances human comfort and efficiency

Provides a versatile framework adaptable to various tasks

Abstract

We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in human-robot collaborative tasks. To this end, we propose CoMOTO, a trajectory optimization framework that utilizes stochastic motion prediction models to anticipate the human's motion and adapt the robot's joint trajectory accordingly. We design a multi-objective cost function that simultaneously optimizes for i) separation distance, ii) visibility of the end-effector, iii) legibility, iv) efficiency, and v) smoothness. We evaluate CoMOTO against three existing methods for robot trajectory generation when in close proximity to humans. Our experimental results indicate that our approach consistently outperforms existing methods over a combined set of safety, comfort, and efficiency metrics.