Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model (2311.03600v2)

Abstract: Learning from demonstration (LfD) provides an efficient way to train robots. The learned motions should be convergent and stable, but to be truly effective in the real world, LfD-capable robots should also be able to remember multiple motion skills. Existing stable-LfD approaches lack the capability of multi-skill retention. Although recent work on continual-LfD has shown that hypernetwork-generated neural ordinary differential equation solvers (NODE) can learn multiple LfD tasks sequentially, this approach lacks stability guarantees. We propose an approach for stable continual-LfD in which a hypernetwork generates two networks: a trajectory learning dynamics model, and a trajectory stabilizing Lyapunov function. The introduction of stability generates convergent trajectories, but more importantly it also greatly improves continual learning performance, especially in the size-efficient chunked hypernetworks. With our approach, a single hypernetwork learns stable trajectories of the robot's end-effector position and orientation simultaneously, and does so continually for a sequence of real-world LfD tasks without retraining on past demonstrations. We also propose stochastic hypernetwork regularization with a single randomly sampled regularization term, which reduces the cumulative training time cost for N tasks from O$(N^2)$ to O$(N)$ without any loss in performance on real-world tasks. We empirically evaluate our approach on the popular LASA dataset, on high-dimensional extensions of LASA (including up to 32 dimensions) to assess scalability, and on a novel extended robotic task dataset (RoboTasks9) to assess real-world performance. In trajectory error metrics, stability metrics and continual learning metrics our approach performs favorably, compared to other baselines. Our open-source code and datasets are available at https://github.com/sayantanauddy/clfd-snode.