Model-Predictive Control with Inverse Statics Optimization for Tensegrity Spine Robots

This project represents specialized academic research into tensegrity robotics, a niche subfield of soft robotics. Despite being nearly 8 years old with zero stars, it offers a specific mathematical approach to a difficult control problem (cables can only pull, not push). The defensibility is low (3) because, while the math is non-trivial, the implementation lacks a community, maintenance, or ease of use for non-experts. Frontier labs are unlikely to compete directly as they favor general-purpose RL for robotics rather than specialized MPC for niche hardware like tensegrity spines. The primary risk is not platform domination, but obsolescence through newer end-to-end Reinforcement Learning (RL) techniques that bypass the need for explicit inverse statics modeling. This is a 'reference implementation' for the associated 2018 arXiv paper and serves as a foundational piece for researchers in the BEST Lab (UC Berkeley) or NASA's NTRT community, but it is not a commercial-grade tool.