Koopman operator regression on physics-simulated cloth data yields a linear surrogate model that enables efficient model predictive control for accurate dynamic folding trajectories on unseen poses in both simulation and real-robot experiments.
Modeling, learning, perception, and control methods for deformable object manipulation
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A frozen LSTM backbone with FiLM-based few-shot context adaptation estimates tip-level contact forces for deformable swabbing tools across nine surface-tool regimes using only wrist-mounted proprioception.
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Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control
Koopman operator regression on physics-simulated cloth data yields a linear surrogate model that enables efficient model predictive control for accurate dynamic folding trajectories on unseen poses in both simulation and real-robot experiments.
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Context-Aware Force Estimation for Deformable Tool Manipulation in Robotic Environmental Swabbing via Few-Shot Continual Adaptation
A frozen LSTM backbone with FiLM-based few-shot context adaptation estimates tip-level contact forces for deformable swabbing tools across nine surface-tool regimes using only wrist-mounted proprioception.