Work

Planning Human-Robot Co-Manipulation With Human Motor Control Objectives and Multi-Component Reaching Strategies

Year: 2024

Type: article

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.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2024.3522760

Authors: Kevin Haninger, Luka Peternel

Institutions: Fraunhofer Institute for Production Systems and Design Technology, Delft University of Technology

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Awards: Social industrial collaborative environments integrating AI, Big Data and Robotics for smart manufacturing, G434337789