Whole-Body Motion Guided Reference Generation for Uncertainty-Aware Compliant
Overview
This research proposes a dual-loop assistance strategy for exoskeletons that couples multimodal trajectory generation with variable admittance control. The method uses whole-body human motion and exos, and incorporates task descriptions as semantic cues. A three-stage training pipeline reduces cross-modal representation mismatch, while trajectory variance provides an uncertainty measure for prediction reliability. Experimental results show reduced muscle activation and metabolic energy expenditure compared to standard baselines.
From arXiv Robotics — research abstracts
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- arXiv Robotics — research abstractsSignalWhole-Body Motion Guided Reference Generation for Uncertainty-Aware Compliant Assistance
This research proposes a dual-loop assistance strategy for exoskeletons that couples multimodal trajectory generation with variable admittance control. The method uses whole-body human motion and exos, and incorporates task descriptions as semantic cues. A three-stage training pipeline reduces cross-modal representation mismatch, while trajectory variance provides an uncertainty measure for prediction reliability. Experimental results show reduced muscle activation and metabolic energy expenditure compared to standard baselines.
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