GenRL: Multimodal-foundation world models for generalization in embodied agents (2406.18043v2)

Abstract: Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task. In contrast, language can specify tasks in a more natural way. Current foundation vision-LLMs (VLMs) generally require fine-tuning or other adaptations to be adopted in embodied contexts, due to the significant domain gap. However, the lack of multimodal data in such domains represents an obstacle to developing foundation models for embodied applications. In this work, we overcome these problems by presenting multimodal-foundation world models, able to connect and align the representation of foundation VLMs with the latent space of generative world models for RL, without any language annotations. The resulting agent learning framework, GenRL, allows one to specify tasks through vision and/or language prompts, ground them in the embodied domain's dynamics, and learn the corresponding behaviors in imagination. As assessed through large-scale multi-task benchmarking in locomotion and manipulation domains, GenRL enables multi-task generalization from language and visual prompts. Furthermore, by introducing a data-free policy learning strategy, our approach lays the groundwork for foundational policy learning using generative world models. Website, code and data: https://mazpie.github.io/genrl/

Multimodal Foundation World Models for Generalist Embodied Agents

The paper "Multimodal Foundation World Models for Generalist Embodied Agents" introduces a reinforcement learning (RL) framework named GenRL, designed to enable the development of generalist agents capable of operating across various embodied domains through multimodal foundation models. The core contribution lies in the ability to connect and align the representations of vision-LLMs (VLMs) with the latent space of generative world models, with an emphasis on vision-only data. This approach mitigates the domain gap typically observed when adapting foundation models for embodiment tasks, a prevalent challenge in scaling-up RL.

Overview of GenRL Framework

The GenRL framework operates by transforming visual and language prompts into latent targets, which are subsequently realized by training agents within the imaginative context of the world model. By deploying a multimodal foundation world model (MFWM), GenRL overcomes the lack of multimodal data in embodied domains and facilitates the grounding of tasks specified by vision or language into the dynamics of the RL domain.

Preliminaries and Background

The paper situates itself within existing literature by acknowledging the difficulties associated with reward design in RL, especially for tasks requiring fine-tuning in dynamic visual environments. The use of VLMs to specify tasks addresses this challenge, but typical approaches necessitate substantial fine-tuning or domain adaptations. Therefore, the authors argue for the necessity of an agent learning framework that can operate effectively with minimal data-related costs.

Methodological Contributions

Key methodological contributions are threefold:

1. World Model for RL: The latent dynamics of the environment are modeled within a compact discrete latent space, leveraging a sequence model to self-predict agent inputs. This facilitates highly efficient optimization of agent actions through imaginary trajectories.
2. Multimodal Foundation World Models (MFWMs): A novel integration where the joint embedding space of a pre-trained VLM is connected and aligned with the latent space of the world model. This connection is achieved via a latent connector and an aligner network, allowing the mapping of multimodal task specifications into latent space, which is critical for seamless task groundings in the RL context.
3. Imaginative Task Behavior Learning: The policy learns to match behaviors to target sequences inferred directly from task prompts by training entirely within the model's imaginative context. This removes dependency on extensive reward-labelled data and facilitates generalization to new tasks.

Experimental Findings and Implications

The framework's efficacy is rigorously assessed through comprehensive multi-task benchmarking across various locomotion and manipulation domains. The empirical outcomes demonstrate GenRL's strong generalization capabilities in extracting and adapting task behaviors from visual or language prompts. Results from the experiment validate:

1. Multi-task Generalization: GenRL shows significant prowess in generalizing across unseen tasks, outperforming conventional image-language and video-language reward baselines.
2. Data-free RL: Notably, GenRL pioneers the concept of data-free RL, wherein the agent, post pre-training, can adapt to novel tasks without requiring additional data. This property is paramount as it mirrors the adaptive strengths of foundation models in vision and language.
3. Training Data Distribution: The paper also highlights the impact of dataset diversity on model performance. Interestingly, data embodying varied exploration experiences contribute substantially to the robust generalization of the model, evidencing the advantage of leveraging unstructured datasets.

Theoretical and Practical Implications

Theoretically, the paper presents a significant advancement in harmonizing the representational spaces of multimodal VLMs and world models, implicitly questioning the prevailing necessity of reward-labelled data in RL. Practically, GenRL showcases compelling potential for developing scalable, adaptable agents capable of understanding and executing complex behaviors based on high-level task specifications. This paradigm shift could drive significant progress in autonomous systems, including robotics and interactive applications.

Future Directions

Future research could expand on several fronts:

• Behavior Composition: Investigating methods to compose learned behaviors into complex, long-horizon tasks.
• Temporal Flexibility: Enhancing the framework to dynamically adjust temporal spans for accurately capturing static and extended actions.
• Model Scalability: Improving the quality of reconstructed observations by exploring more sophisticated architectures, such as transformers or diffusion models.

In conclusion, the GenRL framework serves as a pivotal step towards realizing generalist embodied agents capable of intuitive, multimodal task comprehension and execution, laying the groundwork for future expanses in the RL-driven application space.