Robotics: Science and Systems XXII

Stabilizing 3D Continuum-Arm Rollouts via Equilibrium Anchoring and Feature-Lifted Residual Learning

Ahsan Tanveer, Rahdar Hussain Afridi, Waqar Hussain Afridi, Feitian Zhang, Guangming Xie

Abstract:

Multi-step motion prediction for continuum robots is difficult, especially under actuation distribution shift, where error accumulation can distort the predicted steady response and destabilize rollouts. This paper introduces a hybrid equilibrium-anchored plus residual-learning framework for a tendon-driven 3D continuum arm that makes steady behavior explicit. An equilibrium prior is learned from inexpensive static equilibrium data and used in a contractive update that continuously pulls predictions toward the equilibrium estimate, improving rollout stability. A lightweight feature-lifted residual model, linear in parameters, learns the remaining one-step mismatch from dynamic trajectory data, recovering transient dynamics. The approach is validated on 200-step rollouts under actuation that is stronger and faster than in training. The Hybrid reduces backbone position RMSE by 26% and tip position RMSE by 27%, producing consistent accuracy gains over prior-only and residual-only predictors while remaining stable across all tested trajectories. The same proposed model also improves robustness on standard nonlinear benchmarks against a combined Koopman baseline under matched evaluation protocols.

Download:

Bibtex:

  
@INPROCEEDINGS{TanveerA-RSS-26, 
    AUTHOR    = {Ahsan Tanveer AND Rahdar Hussain Afridi AND Waqar Hussain Afridi AND Feitian Zhang AND Guangming Xie}, 
    TITLE     = {{Stabilizing 3D Continuum-Arm Rollouts via Equilibrium Anchoring and Feature-Lifted Residual Learning}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.106} 
}