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.
Efficient jacobian-based inverse kinematics with sim-to-real transfer of soft robots by learning
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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.