Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics

Abstract:This work addresses open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics. Visual Oscillator Networks (VONs) from previous work are used, which provide mechanically interpretable 2D oscillator latents through an attention broadcast decoder (ABCD). Open-loop, single-shooting optimal control is performed in latent space to track image-specified waypoints without camera feedback. An interactive SCR live simulator enables design of static, dynamic, and extrapolated targets and maps them to model-specific latent waypoints. On a two-segment pneumatic SCR, Koopman, MLP, and oscillator dynamics, each with and without ABCD, are evaluated on setpoint and dynamic trajectories. ABCD-based models consistently reduce image-space tracking error. The VON and ABCD-based Koopman models attain the lowest mean squared errors (MSEs). Ablation and simulation stress tests confirm that training and architecture choices support open-loop performance, including static holding, stable extrapolated equilibria, and relaxation to rest. To the best of our knowledge, this is the first demonstration of reliable long-horizon open-loop control of a physical soft robot using interpretable latent dynamics models learned solely from video.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2603.19655 [cs.RO]

(or arXiv:2603.19655v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2603.19655 Focus to learn more arXiv-issued DOI via DataCite

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