Summary of Task-conditioned Adaptation Of Visual Features in Multi-task Policy Learning, by Pierre Marza et al.
Task-conditioned adaptation of visual features in multi-task policy learning
by Pierre Marza, Laetitia Matignon, Olivier Simonin, Christian Wolf
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 presents a novel approach to multi-task learning for autonomous agents, which requires adaptability in decision-making strategies as well as perception modules. The authors draw an analogy between human visual perception and their proposed solution, conditioning pre-trained large vision models on specific downstream tasks. They introduce task-conditioned adapters that do not require fine-tuning any pre-trained weights, combined with a single policy trained with behavior cloning and capable of addressing multiple tasks. The adapters are conditioned on task embeddings, which can be selected at inference or inferred from example demonstrations using an optimization-based estimator. The method is evaluated on the CortexBench benchmark and shows that it can address a wide variety of tasks with a single policy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots learn many skills at once, like humans do. It’s called multi-task learning. The authors want to make sure the robot can see and understand what’s happening in its environment better. They came up with a way to use pre-trained images and adapt them for specific tasks. This allows the robot to learn many things from just a few examples. They tested it on lots of different tasks and showed that it works well, even when trying something new. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Multi task * Optimization
