Summary of Contractive Dynamical Imitation Policies For Efficient Out-of-sample Recovery, by Amin Abyaneh et al.
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
by Amin Abyaneh, Mahrokh G. Boroujeni, Hsiu-Chin Lin, Giancarlo Ferrari-Trecate
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Machine Learning (stat.ML)
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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 In this paper, researchers propose a new approach to imitation learning that addresses issues with unreliable outcomes in out-of-sample regions. The traditional method, which relies on stable dynamical systems, can guarantee convergence to a desired state but often overlooks transient behavior. To overcome this limitation, the authors introduce a framework that uses contractive dynamical systems to ensure policy rollouts converge regardless of perturbations, enabling efficient out-of-sample recovery. This is achieved by leveraging recurrent equilibrium networks and coupling layers, which guarantee contractivity for any parameter choice, facilitating unconstrained optimization. The authors also provide theoretical upper bounds for worst-case and expected loss terms, establishing the reliability of their method in deployment. Empirically, they demonstrate significant performance improvements in robotics manipulation and navigation tasks in simulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from experts that works better when you’re outside the training data. The old approach is good at getting to a goal state, but it doesn’t do well with temporary problems. To fix this, the researchers created a system that uses a special type of dynamic model to make sure the learning policy works everywhere, not just where it was trained. They tested their method on robotic tasks and showed big improvements in how well it worked. |
Keywords
» Artificial intelligence » Optimization
