Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model
CoRR(2023)
摘要
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.
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关键词
efficient continual learning,dynamics,hypernetwork-generated
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