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This paper presents a novel Learning from Demonstration (LfD) method thatuses neural fields to learn new skills efficiently and accurately. It achievesthis by utilizing a shared embedding to learn both scene and motionrepresentations in a generative way. Our method smoothly maps each expertdemonstration to a scene-motion embedding and learns to model them withoutrequiring hand-crafted task parameters or large datasets. It achieves dataefficiency by enforcing scene and motion generation to be smooth with respectto changes in the embedding space. At inference time, our method can retrievescene-motion embeddings using test time optimization, and generate precisemotion trajectories for novel scenes. The proposed method is versatile and canemploy images, 3D shapes, and any other scene representations that can bemodeled using neural fields. Additionally, it can generate both end-effectorpositions and joint angle-based trajectories. Our method is evaluated on tasksthat require accurate motion trajectory generation, where the underlying taskparametrization is based on object positions and geometric scene changes.Experimental results demonstrate that the proposed method outperforms thebaseline approaches and generalizes to novel scenes. Furthermore, in real-worldexperiments, we show that our method can successfully model multi-valuedtrajectories, it is robust to the distractor objects introduced at inferencetime, and it can generate 6D motions.

Neural Field Movement Primitives for Joint Modelling of Scenes and Motions