Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations
This work addresses stable locomotion control for quadruped robots, which is crucial for real-world applications like search and rescue or logistics, but it is incremental as it builds on existing learning-based techniques.
The paper tackles the problem of generating stable leg trajectories for quadruped robots under varying target velocities and turning radii by proposing a two-pronged approach: training simpler policies for discrete velocities and using a higher-level neural network filter to enable smooth transitions, learned from expert demonstrations. The method requires fewer expert demonstrations than standard architectures and is demonstrated experimentally on the Stoch 2 robot.
With the research into development of quadruped robots picking up pace, learning based techniques are being explored for developing locomotion controllers for such robots. A key problem is to generate leg trajectories for continuously varying target linear and angular velocities, in a stable manner. In this paper, we propose a two pronged approach to address this problem. First, multiple simpler policies are trained to generate trajectories for a discrete set of target velocities and turning radius. These policies are then augmented using a higher level neural network for handling the transition between the learned trajectories. Specifically, we develop a neural network-based filter that takes in target velocity, radius and transforms them into new commands that enable smooth transitions to the new trajectory. This transformation is achieved by learning from expert demonstrations. An application of this is the transformation of a novice user's input into an expert user's input, thereby ensuring stable manoeuvres regardless of the user's experience. Training our proposed architecture requires much less expert demonstrations compared to standard neural network architectures. Finally, we demonstrate experimentally these results in the in-house quadruped Stoch 2.