Summary of Efficient Policy Adaptation with Contrastive Prompt Ensemble For Embodied Agents, by Wonje Choi et al.
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
by Wonje Choi, Woo Kyung Kim, SeungHyun Kim, Honguk Woo
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The novel contrastive prompt ensemble (ConPE) framework enables rapid policy adaptation to unseen visual observations for embodied reinforcement learning (RL) agents. The framework utilizes a pretrained vision-language model and a set of visual prompts to construct robust state representations, which generalize to various domains and optimize task learning. ConPE outperforms other state-of-the-art algorithms for tasks like navigation in AI2THOR, manipulation in egocentric-Metaworld, and autonomous driving in CARLA, while improving sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Embodied robots need to adapt quickly to new situations. A team of researchers created a new way to make this happen using computer vision and language models. They combined these models with special prompts that help the robot understand its surroundings. This new approach works well for different tasks like navigating through buildings or controlling robots in virtual environments. It’s faster and more efficient than other methods, making it promising for real-world applications. |
Keywords
» Artificial intelligence » Language model » Prompt » Reinforcement learning
