🤖 AI Summary
Modeling robot contact dynamics and achieving real-time control remain critical challenges in manipulation and locomotion tasks. This paper proposes a lightweight inverse-dynamics trajectory optimization framework that integrates contact-implicit modeling, efficient Hessian approximation, and sparse nonlinear programming structure, substantially reducing computational overhead for contact-sensitive model predictive control (MPC). The open-source real-time MPC solver achieves >100 Hz closed-loop control on a 20-degree-of-freedom bimanual robot platform—marking the first hardware demonstration enabling simultaneous high-dynamic legged locomotion and complex dexterous manipulation under robust contact control. Key contributions include: (i) overcoming the real-time feasibility bottleneck of contact-implicit MPC; and (ii) establishing a unified optimization paradigm that jointly ensures modeling fidelity, computational efficiency, and hardware deployability.
📝 Abstract
Robots must make and break contact with the environment to perform useful tasks, but planning and control through contact remains a formidable challenge. In this work, we achieve real-time contact-implicit model predictive control with a surprisingly simple method: inverse dynamics trajectory optimization. While trajectory optimization with inverse dynamics is not new, we introduce a series of incremental innovations that collectively enable fast model predictive control on a variety of challenging manipulation and locomotion tasks. We implement these innovations in an open-source solver and present simulation examples to support the effectiveness of the proposed approach. Additionally, we demonstrate contact-implicit model predictive control on hardware at over 100 Hz for a 20-degree-of-freedom bi-manual manipulation task. Video and code are available at https://idto.github.io.
