Summary of Genrl: Multimodal-foundation World Models For Generalization in Embodied Agents, by Pietro Mazzaglia et al.
GenRL: Multimodal-foundation world models for generalization in embodied agents
by Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron Courville, Sai Rajeswar
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 tackles the problem of developing generalist embodied agents that can solve various tasks across different domains. The authors argue that reinforcement learning (RL) is difficult to scale up due to the need for complex reward designs, whereas language can specify tasks in a more natural way. Current foundation vision-language models (VLMs) require fine-tuning or adaptations to be used in embodied contexts, but lack multimodal data hinders developing foundation models for embodied applications. The paper presents multimodal-foundation world models that connect and align VLM representations with generative world model latent spaces without language annotations. This leads to the GenRL agent learning framework, which allows task specification through vision and/or language prompts, grounding them in domain dynamics, and learning corresponding behaviors in imagination. The authors demonstrate multi-task generalization from language and visual prompts on large-scale benchmarks in locomotion and manipulation domains. Additionally, they introduce a data-free policy learning strategy for foundational policy learning using generative world models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching robots to do many things, like moving around and picking up objects. The problem is that current methods are hard to use because they require a lot of specific information for each task. The authors propose a new way to teach robots by combining visual and language prompts. They show that their approach can be used to learn many tasks at once and perform well on a variety of challenges. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Grounding » Multi task » Reinforcement learning
