Learning to Guide Online Multi-Contact Receding Horizon Planning
| Type of publication: | Conference paper |
| Citation: | Wang_IROS2022_2022 |
| Publication status: | Accepted |
| Booktitle: | Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS) |
| Year: | 2022 |
| Abstract: | When planning multi-contact motions in a receding horizon fashion, it is critical that the motion to be executed facilitates the completion of the task. This requires a value function to inform the desirability of robot states. However, given the complex dynamics, value functions are often approximated by expensive computation of trajectories in an extended planning horizon. In this work, to achieve online multi-contact Receding Horizon Planning (RHP), we propose to learn an oracle, which can predict local objectives (intermediate goals) for a given task based on the current robot state and the environment. Then, we use these local objectives to construct local value functions to guide a short-horizon RHP. To generalize across environments, we use a novel robot-centric representation of oracle variables. We also present an incremental training scheme, that can improve the prediction accuracy by adding demonstrations on how to recover from failures. We compare our approach against the baseline (long-horizon RHP) for planning centroidal trajectories of humanoid walking on moderate slopes, as well as large slopes where static stability cannot be achieved. We validate these trajectories by tracking them via a whole-body inverse dynamics controller in simulation. We show that our approach can achieve online RHP for 95%-98.6% cycles, outperforming the baseline (8%-51.2%). |
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| Added by: | [UNK] |
| Total mark: | 0 |
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