Summary of Visual Grounding For Object-level Generalization in Reinforcement Learning, by Haobin Jiang et al.
Visual Grounding for Object-Level Generalization in Reinforcement Learning
by Haobin Jiang, Zongqing Lu
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 tackle the challenge of generalization for agents following natural language instructions. They leverage a vision-language model (VLM) to achieve visual grounding and transfer its knowledge into reinforcement learning (RL) for object-centric tasks. This allows the agent to generalize to unseen objects and instructions without prior training. The authors propose two routes to transfer VLM knowledge: an intrinsic reward function derived from the confidence map, which guides the agent towards the target object; and a unified task representation using the confidence map, enabling the agent to process unseen objects and instructions. Experimental results show that the proposed approach improves performance on challenging skill learning and exhibits better generalization capabilities in multi-task experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps agents follow natural language instructions by using a special kind of AI model called a vision-language model (VLM). The VLM helps the agent understand what it’s being asked to do, even if it’s never seen the object or instruction before. To make this work, the researchers created two new ways for the agent to use the VLM’s knowledge: a reward system that tells the agent how well it’s doing, and a way to represent the task that makes sense visually. This allows the agent to learn new skills and apply them to objects it hasn’t seen before. |
Keywords
* Artificial intelligence * Generalization * Grounding * Language model * Multi task * Reinforcement learning
