robotics
The Robot Anticipated Its Human Moving Partner
Predicting shared-object motion reduced effort in 108 collaborative transport trials.
Summary
Predicting shared-object motion reduced effort in 108 collaborative transport trials.
PROACT combines a learned prediction of human collaborative behavior with compliant whole-body control for carrying a shared object. Across 108 real-world trials with a nine-degree-of-freedom mobile manipulator, it reduced mean interaction work by 59.2% against a compliance-only baseline and 20.4% against model-predictive control. Completion time also fell by 12.9% and 6.9%, respectively. The result applies to the study's dyadic transport tasks and hardware.
Why it matters
Predicting shared-object motion reduced effort in 108 collaborative transport trials.
Limits and context
- Across 108 real-world trials with a nine-degree-of-freedom mobile manipulator, it reduced mean interaction work by 59.2% against a compliance-only baseline and 20.4% against model-predictive control.
Key claims
Predicting shared-object motion reduced effort in 108 collaborative transport trials.
Qualification: Across 108 real-world trials with a nine-degree-of-freedom mobile manipulator, it reduced mean interaction work by 59.2% against a compliance-only baseline and 20.4% against model-predictive control.
Evidence: source-2026-09-23-009
Sources
- arXiv preprint 2609.25351arXiv · primary research
Corrections
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