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.
High-precision dynamic control of soft robots with the physics-learning hybrid modeling approach
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
baseline 1
citation-polarity summary
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1roles
baseline 1polarities
baseline 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.