Summary of M2distill: Multi-modal Distillation For Lifelong Imitation Learning, by Kaushik Roy et al.
M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning
by Kaushik Roy, Akila Dissanayake, Brendan Tidd, Peyman Moghadam
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 A novel approach to lifelong imitation learning is presented in this paper. The challenge lies in distribution shifts occurring during incremental learning steps, which can lead to scalability issues and catastrophic forgetting of previously learned skills. To address this, the authors introduce M2Distill, a multi-modal distillation-based method that focuses on preserving a consistent latent space across vision, language, and action distributions. By regulating these shifts and reducing discrepancies in Gaussian Mixture Model policies, the learned policy can retain its ability to perform previous tasks while integrating new skills. The method is evaluated on the LIBERO lifelong imitation learning benchmark suites, demonstrating state-of-the-art performance across all metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lifelong learning for robots is a big deal! Imagine if your robot friend could learn new things as you do. This paper talks about how to make that happen. It’s hard because the robot might forget what it learned before, and it needs to remember. The authors came up with a solution called M2Distill. They tested it on some robots and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Distillation » Latent space » Mixture model » Multi modal
