Bio-Inspired Contraction Theory for Resilient Motion Planning

Singh et al. (2023) used contraction theory for robust feedback planning, but biological systems achieve resilience through hierarchical reflex mechanisms absent in current frameworks. This research proposes a neuro-mechanical contraction planner that integrates spinal cord-inspired feedback loops (e.g., central pattern generators) with Control Contraction Metrics. Unlike purely analytical approaches (Singh et al.), the system uses EMG-IMU sensor fusion (Wang et al., 2024) to detect perturbations and trigger biologically plausible reflex adjustments—such as instantaneous joint stiffening or trajectory replanning—without full MPC reoptimization. This could revolutionize navigation in unpredictable environments (e.g., disaster sites) by combining the rigor of contraction theory with the adaptability of biological systems.

References:

1. Robust feedback motion planning via contraction theory. Sumeet Singh, Benoit Landry, Anirudha Majumdar, J. Slotine, M. Pavone (2023). Int. J. Robotics Res..
2. Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask Learning. Enkai Wang, Xingjian Chen, Yuge Li, Zhongzheng Fu, Jian Huang (2024). IEEE transactions on fuzzy systems.