Predictive and Robust Robot Assistance for Sequential Manipulation

TL;DR

This paper introduces a predictive, hierarchical optimization framework for robot assistance in sequential manipulation tasks, enabling proactive support for impaired users by forecasting their actions and maintaining task consistency.

Contribution

The paper presents a novel prediction formulation and constancy constraints for robot assistance, allowing concurrent future path consideration and dependency modeling in sequential tasks.

Findings

Effective prediction of user behavior in manipulation tasks

Successful robot support in simulated and real experiments

Improved task performance with proactive assistance

Abstract

This paper presents a novel concept to support physically impaired humans in daily object manipulation tasks with a robot. Given a user's manipulation sequence, we propose a predictive model that uniquely casts the user's sequential behavior as well as a robot support intervention into a hierarchical multi-objective optimization problem. A major contribution is the prediction formulation, which allows to consider several different future paths concurrently. The second contribution is the encoding of a general notion of constancy constraints, which allows to consider dependencies between consecutive or far apart keyframes (in time or space) of a sequential task. We perform numerical studies, simulations and robot experiments to analyse and evaluate the proposed method in several table top tasks where a robot supports impaired users by predicting their posture and proactively re-arranging objects.