Human-Robot Collaborative Carrying of Objects with Unknown Deformation Characteristics
This addresses the challenge of flexible human-robot collaboration in real-world tasks like logistics or healthcare, though it is incremental as it builds on existing control methods.
The paper tackles the problem of human-robot collaborative carrying of objects with unknown deformation characteristics by introducing an adaptive control framework that uses haptic and kinematic inputs to generate reactive motions, and experimental results with 12 subjects show it effectively handles such objects and provides intuitive assistance.
In this work, we introduce an adaptive control framework for human-robot collaborative transportation of objects with unknown deformation behaviour. The proposed framework takes as input the haptic information transmitted through the object, and the kinematic information of the human body obtained from a motion capture system to create reactive whole-body motions on a mobile collaborative robot. In order to validate our framework experimentally, we compared its performance with an admittance controller during a co-transportation task of a partially deformable object. We additionally demonstrate the potential of the framework while co-transporting rigid (aluminum rod) and highly deformable (rope) objects. A mobile manipulator which consists of an Omni-directional mobile base, a collaborative robotic arm, and a robotic hand is used as the robotic partner in the experiments. Quantitative and qualitative results of a 12-subjects experiment show that the proposed framework can effectively deal with objects of unknown deformability and provides intuitive assistance to human partners.