aiqbench analysis
This research introduces PROACT, a transformer-based framework that integrates learned human behavior predictions into compliant robot control for collaborative object transport. By anticipating partner movements rather than relying solely on reactive compliance or rigid motion planning, the system significantly reduces user effort and task completion time in real-world trials. The work advances human-robot interaction by demonstrating how machine learning models can distill complex dyadic behaviors to create more intuitive and efficient robotic assistants.
Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport
arXiv:2609.25351v1 Announce Type: new Abstract: We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a user and a robot work together to relocate a large or heavy object. To act as an effective partner, the robot should reduce the user's effort by contributing to efficient relocation of the object while remaining physically responsive to them. Prior work often addresses these capabilities separately, producing robots that may move the object efficiently but resist user input, or accommodate the user but depend on continuous guidance. Our key insight is that obstacle-constrained collaborative transport requires integrating predictions of human collaborative behavior with compliant robot control. To this end, we introduce PROACT, a framework for human-robot collaborative transport that incorporates anticipation into compliant whole-body control through a learned model of human collaborative behavior. Trained on a large-scale, real-world dataset of dyadic human transport demonstrations, our transformer architecture distills collaborative behavior into predictions of future object motion. Across 108 real-world trials with a 9-DoF mobile manipulator, PROACT reduces mean interaction work by 59.2\% and 20.4\%, and mean completion time by 12.9\% and 6.9\%, relative to compliance-only and MPC baselines, respectively. Footage from our experiments can be found at https://youtu.be/qAGvQfVPjbk.
Abstract:We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a user and a robot work together to relocate a large or heavy object. To act as an effective partner, the robot should reduce the user's effort by contributing to efficient relocation of the object while remaining physically responsive to them. Prior work often addresses these capabilities separately, producing robots that may move the object efficiently but resist user input, or accommodate the user but depend on continuous guidance. Our key insight is that obstacle-constrained collaborative transport requires integrating predictions of human collaborative behavior with compliant robot control. To this end, we introduce PROACT, a framework for human-robot collaborative transport that incorporates anticipation into compliant whole-body control through a learned model of human collaborative behavior. Trained on a large-scale, real-world dataset of dyadic human transport demonstrations, our transformer architecture distills collaborative behavior into predictions of future object motion. Across 108 real-world trials with a 9-DoF mobile manipulator, PROACT reduces mean interaction work by 59.2\% and 20.4\%, and mean completion time by 12.9\% and 6.9\%, relative to compliance-only and MPC baselines, respectively. Footage from our experiments can be found at this https URL.
Robotics (cs.RO); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
arXiv:2609.25351 [cs.RO]
(or arXiv:2609.25351v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.25351 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 301 words
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