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Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg
Accepted to Conference on Robot Learning (CoRL), 2020
Abstract
Abstract
We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The system consists of a high-level controller that learns to choose from a set of primitives in response to changes in the environment and a low-level controller that utilizes an established control method to robustly execute the primitives. Our framework learns a controller that can adapt to challenging environmental changes on the fly, including novel scenarios not seen during training. The learned controller is up to 85~percent more energy efficient and is more robust compared to baseline methods. We also deploy the controller on a physical robot without any randomization or adaptation scheme.
Contributions
Contributions
We introduce a hierarchical control structure that combines model-based control design and model-free reinforcement learning for legged locomotion.
We demonstrate that our framework allows sample-efficient learning, zero-shot adaptation to novel scenarios, and direct sim-to-real transfer without randomization or adaptation schemes.
Our framework learns adaptive contact sequences that are not present in either model-based or learning-based methods in real-time control. This is evidenced in the natural-looking behaviors that minimize unnecessary movement and energy usage in our split-belt treadmill scenarios.
Overview of the system
Overview of the system
Left: Primitives are distinguished by the contact configuration. The stance legs in each primitive are colored orange.
Center: Hierarchical structure of the controller. The high-level controller chooses from a set of primitives based on the robot state, and the low-level controller computes the motor torques based on the robot state and the primitive chosen.
Right: The low-level controller uses stance foot forces to control the base pose and moves the swing feet to their target positions.
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