Offline motion libraries and online MPC for advanced mobility skills

Our robot ANYmal combines offline motion libraries and online model predictive control for complex locomotion skills. Journal article published in the International Journal of Robotics Research (IJRR): Learn more about the robot at Video by Marko Bjelonic, Title: Offline motion libraries and online MPC for advanced mobility skills Authors: Marko Bjelonic, Ruben Grandia, Moritz Geilinger, Oliver Harley, Vivian S. Medeiros, Vuk Pajovic, Edo Jelavic, Stelian Coros and Marco Hutter Abstract: We describe an optimization-based framework to perform complex locomotion skills for robots with legs and wheels. The generation of complex motions over a long-time horizon often requires offline computation due to current computing constraints and is mostly accomplished through trajectory optimization (TO). In contrast, model predictive control (MPC) focuses on the online computation of trajectories, robust even in the presence of uncertainty, albeit mostly over shorter time horizons and is prone to generating nonoptimal solutions over the horizon of the task’s goals. Our article’s contributions overcome this trade-off by combining offline motion libraries and online MPC, uniting a complex, long-time horizon plan with reactive, short-time horizon solutions. We start from offline trajectories that can be, for example, generated by TO or sampling-based methods. Also, multiple offline trajectories can be composed out of a motion library into a single maneuver. We then use these offline trajectories as the cost for the online MPC, allowing us to smoothly blend between multiple composed motions even in the presence of discontinuous transitions. The MPC optimizes from the measured state, resulting in feedback control, which robustifies the task’s execution by reacting to disturbances and looking ahead at the offline trajectory. With our contribution, motion designers can choose their favorite method to iterate over behavior designs offline without tuning robot experiments, enabling them to author new behaviors rapidly. Our experiments demonstrate complex and dynamic motions on our traditional quadrupedal robot ANYmal and its roller-walking version. Moreover, the article’s findings contribute to evaluating five planning algorithms. Video content: - 00:00​ Boston Dynamic’s dream - 00:13 Intro - 00:20​ Dance - 00:35 Summary - 01:15 Approach - 02:47 Outro Acknowledgments: This work was supported in part by the Swiss National Science Foundation (SNF) through the National Centres of Competence in Research Robotics (NCCR Robotics) and Digital Fabrication (NCCR dfab). Besides, it has been conducted as part of ANYmal Research, a community to advance legged robotics. Disclaimer: Robot from ANYbotics; customized by ETH Zürich; strictly for research purposes.