Zero-Shot Generalization from Motion Demonstrations to New Tasks
This work addresses the challenge of efficient and stable motion policy generalization in robotics, offering a novel approach that is incremental in combining existing methods.
The paper tackles the problem of reusing isolated motion demonstrations for new tasks in robotics by introducing the Gaussian Graph to bridge continuous control with discrete graph search, enabling zero-shot generalization to unseen tasks where baseline methods fail.
Learning motion policies from expert demonstrations is an essential paradigm in modern robotics. While end-to-end models aim for broad generalization, they require large datasets and computationally heavy inference. Conversely, learning dynamical systems (DS) provides fast, reactive, and provably stable control from very few demonstrations. However, existing DS learning methods typically model isolated tasks and struggle to reuse demonstrations for novel behaviors. In this work, we formalize the problem of combining isolated demonstrations within a shared workspace to enable generalization to unseen tasks. The Gaussian Graph is introduced, which reinterprets spatial components of learned motion primitives as discrete vertices with connections to one another. This formulation allows us to bridge continuous control with discrete graph search. We propose two frameworks leveraging this graph: Stitching, for constructing time-invariant DSs, and Chaining, giving a sequence-based DS for complex motions while retaining convergence guarantees. Simulations and real-robot experiments show that these methods successfully generalize to new tasks where baseline methods fail.