Model-based trajectory optimization often fails to find a reference trajectory for under-actuated bipedal robots performing highly-dynamic, contact-rich tasks in the real world due to inaccurate physical models. In this paper, we propose a complete system that automatically designs a reference trajectory that succeeds on tasks in the real world with a very small number of real world experiments. We adopt existing system identification techniques and show that, with appropriate model parameterization and control optimization, an iterative system identification framework can be effective for designing reference trajectories. We focus on a set of tasks that leverage the momentum transfer strategy to rapidly change the whole body from an initial configuration to a target configuration
by generating large accelerations at the center of mass and switching contacts.
We thank the anonymous reviewers for their helpful comments. We want to thank Greg Turk, Frank Dellaert, James O’Brien, Jarek Rossignac, Sehoon Ha, Yufei Bai and Yuting Gu for their help on this research. This research is funded by NIH grant R01 114149, NSF EFRI-M3C 1137229 and NSF IIS-1064983.