HareshKarnan/tensgrity_dbar_IROS

This project is a classic research artifact from an IROS (International Conference on Intelligent Robots and Systems) publication. With 0 stars and no activity for nearly 8 years, it serves purely as a historical reference for researchers studying tensegrity (tension-integrity) structures. Tensegrity robots are a niche area of soft/flexible robotics characterized by non-linear dynamics and high degrees of freedom, making traditional control difficult. From a competitive standpoint, the project has no moat; it is highly specific to a particular 'Dbar' hardware configuration that is likely no longer in use. While tensegrity remains an active academic interest (e.g., NASA's SUPERball or Berkeley's BEST Lab), modern approaches have largely shifted from the classical visual feedback control seen here toward Reinforcement Learning (RL) and Sim-to-Real transfer. Frontier labs like OpenAI or Google DeepMind have no interest in such niche hardware-specific controllers, as they are focused on foundation models for generalized robotics (e.g., RT-2). The displacement horizon is set to '6 months' because the methodology is effectively already obsolete in the context of current state-of-the-art robotics research.