Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangement
CoRR(2024)
摘要
Task-oriented object grasping and rearrangement are critical skills for
robots to accomplish different real-world manipulation tasks. However, they
remain challenging due to partial observations of the objects and shape
variations in categorical objects. In this paper, we propose the Multi-feature
Implicit Model (MIMO), a novel object representation that encodes multiple
spatial features between a point and an object in an implicit neural field.
Training such a model on multiple features ensures that it embeds the object
shapes consistently in different aspects, thus improving its performance in
object shape reconstruction from partial observation, shape similarity measure,
and modeling spatial relations between objects. Based on MIMO, we propose a
framework to learn task-oriented object grasping and rearrangement from single
or multiple human demonstration videos. The evaluations in simulation show that
our approach outperforms the state-of-the-art methods for multi- and
single-view observations. Real-world experiments demonstrate the efficacy of
our approach in one- and few-shot imitation learning of manipulation tasks.
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