Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Hierarchical learning has been successful at learning generalizable locomotion skills on walking robots in a sample-efficient manner. However, the low-dimensional 'latent' action used to communicate between two layers of the hierarchy is typically user-designed. In this letter, we present a fully-learned hierarchical framework, that is capable of jointly learning the low-level controller and the high-level latent action space. Once this latent space is learned, we plan over continuous latent actions in a model-predictive control fashion, using a learned high-level dynamics model. This framework generalizes to multiple robots, and we present results on a Daisy hexapod simulation, A1 quadruped simulation, and Daisy robot hardware. We compare a range of learned hierarchical approaches from literature, and show that our framework outperforms baselines on multiple tasks and two simulations. In addition to learning approaches, we also compare to inverse-kinematics (IK) acting on desired robot motion, and show that our fully-learned framework outperforms IK in adverse settings on both A1 and Daisy simulations. On hardware, we show the Daisy hexapod achieve multiple locomotion tasks, in an unstructured outdoor setting, with only 2000 hardware samples, reinforcing the robustness and sample-efficiency of our approach.
Details
Original language | English |
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Article number | 9363629 |
Pages (from-to) | 2682-2689 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 6 |
Issue number | 2 |
Publication status | Published - Apr 2021 |
Peer-reviewed | Yes |
Externally published | Yes |
External IDs
ORCID | /0000-0001-9430-8433/work/158768046 |
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Keywords
ASJC Scopus subject areas
Keywords
- Legged locomotion, motion planning, robot control, robot learning