Planning Human-Robot Co-manipulation with Human Motor Control Objectives and Multi-component Reaching Strategies

TL;DR

This paper introduces a novel approach for human-robot co-manipulation that integrates human motor control models into robot planning, enabling more natural and adaptable collaborative movements based on well-studied human behavior principles.

Contribution

It develops a trajectory optimization framework incorporating human motor control models and multi-component reaching strategies for improved collaborative robot motion planning.

Findings

Successfully produced human-like trajectories in physical collaboration tasks.

Demonstrated adaptability to uncertain goal-reaching scenarios.

Validated approach across various conditions with synchronized motion.

Abstract

For successful goal-directed human-robot interaction, the robot should adapt to the intentions and actions of the collaborating human. This can be supported by musculoskeletal or data-driven human models, where the former are limited to lower-level functioning such as ergonomics, and the latter have limited generalizability or data efficiency. What is missing, is the inclusion of human motor control models that can provide generalizable human behavior estimates and integrate into robot planning methods. We use well-studied models from human motor control based on the speed-accuracy and cost-benefit trade-offs to plan collaborative robot motions. In these models, the human trajectory minimizes an objective function, a formulation we adapt to numerical trajectory optimization. This can then be extended with constraints and new variables to realize collaborative motion planning and goal estimation. We deploy this model, as well as a multi-component movement strategy, in physical collaboration with uncertain goal-reaching and synchronized motion tasks, showing the ability of the approach to produce human-like trajectories over a range of conditions.