A conditional rectified flow matching framework learns inverse dynamics for soft robots as a generative map, cutting trajectory tracking error by more than half versus MLP, LSTM, and Transformer baselines while enabling stable high-speed open-loop execution.
Adaptive model-predictive control of a soft continuum robot using a physics-informed neural network based on cosserat rod theory
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ABCD and VONs enable visually and mechanically interpretable latent dynamics learning for soft robots from video, with reported multi-step prediction gains of 5.8x and 3.5x on two-segment cases.
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A Flow Matching Framework for Soft-Robot Inverse Dynamics
A conditional rectified flow matching framework learns inverse dynamics for soft robots as a generative map, cutting trajectory tracking error by more than half versus MLP, LSTM, and Transformer baselines while enabling stable high-speed open-loop execution.
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Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video
ABCD and VONs enable visually and mechanically interpretable latent dynamics learning for soft robots from video, with reported multi-step prediction gains of 5.8x and 3.5x on two-segment cases.