ForPC: Force-Aware Whole-Body Control for Precise Humanoid Cart Pushing
Overview
This paper introduces ForPC, a force-aware whole-body control framework for humanoid robots designed to push wheeled carts in manufacturing and logistics settings. The method uses two-stage reinforcement learning to regulate cart velocity while maintaining balance under varying loads and postures. Trained entirely in simulation, the policy transfers to a physical Unitree G1 robot without additional training or fine-tuning, successfully pushing carts carrying up to 30 kg at 0.5 m/s. Experiments show significant improvements over baseline methods in both cart velocity tracking and cross-track error on multi-bend routes.
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Event evidence and corrections
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Reported quantity · cart payload capacity: 30 other · Basis not reported
“motion-only baseline (Loco-only) and cross-track RMSE by up to 74.2% on the multi-bend route. Trained solely in simulation, the policy also transfers to a physical Unitree G1 without real-world training or fine-tuning and, with no payload-specific adjustment, pushes a cart carrying up to 30 kg at 0.5 m/s. These results suggest that humanoid robots could eventually replace humans for efficient material handling in dai”
Exact source · revision 1Source owner not reported
Reported quantity · cart pushing speed: 0.5 other · Basis not reported
“motion-only baseline (Loco-only) and cross-track RMSE by up to 74.2% on the multi-bend route. Trained solely in simulation, the policy also transfers to a physical Unitree G1 without real-world training or fine-tuning and, with no payload-specific adjustment, pushes a cart carrying up to 30 kg at 0.5 m/s. These results suggest that humanoid robots could eventually replace humans for efficient material handling in dai”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalForPC: Force-Aware Whole-Body Control for Precise Humanoid Cart Pushing
This paper introduces ForPC, a force-aware whole-body control framework for humanoid robots designed to push wheeled carts in manufacturing and logistics settings. The method uses two-stage reinforcement learning to regulate cart velocity while maintaining balance under varying loads and postures. Trained entirely in simulation, the policy transfers to a physical Unitree G1 robot without additional training or fine-tuning, successfully pushing carts carrying up to 30 kg at 0.5 m/s. Experiments show significant improvements over baseline methods in both cart velocity tracking and cross-track error on multi-bend routes.
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