Summary of Meta-controller: Few-shot Imitation Of Unseen Embodiments and Tasks in Continuous Control, by Seongwoong Cho et al.
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
by Seongwoong Cho, Donggyun Kim, Jinwoo Lee, Seunghoon Hong
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel few-shot behavior cloning framework for adaptive robotic systems to simultaneously generalize to unseen embodiments and tasks using a few reward-free demonstrations. The framework leverages a joint-level input-output representation to unify state and action spaces across heterogeneous embodiments, employing a structure-motion state encoder that captures shared knowledge across all embodiments and embodiment-specific knowledge. A matching-based policy network then predicts actions from a few demonstrations, producing an adaptive policy robust to over-fitting. Evaluated in the DeepMind Control suite, the framework, termed Meta-Controller, demonstrates superior few-shot generalization to unseen embodiments and tasks over modular policy learning and few-shot imitation learning approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots learn new skills by showing them a few examples of how to do things. The robot can then use these examples to figure out how to do similar things in the future, even if it’s never seen that specific task or robot body before. The authors created a new way for the robot to learn from just a few demonstrations and tested it on several different robotic tasks. |
Keywords
» Artificial intelligence » Encoder » Few shot » Generalization
