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
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 2roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.