Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control (2405.05852v1)

Abstract: Embodied AI agents require a fine-grained understanding of the physical world mediated through visual and language inputs. Such capabilities are difficult to learn solely from task-specific data. This has led to the emergence of pre-trained vision-LLMs as a tool for transferring representations learned from internet-scale data to downstream tasks and new domains. However, commonly used contrastively trained representations such as in CLIP have been shown to fail at enabling embodied agents to gain a sufficiently fine-grained scene understanding -- a capability vital for control. To address this shortcoming, we consider representations from pre-trained text-to-image diffusion models, which are explicitly optimized to generate images from text prompts and as such, contain text-conditioned representations that reflect highly fine-grained visuo-spatial information. Using pre-trained text-to-image diffusion models, we construct Stable Control Representations which allow learning downstream control policies that generalize to complex, open-ended environments. We show that policies learned using Stable Control Representations are competitive with state-of-the-art representation learning approaches across a broad range of simulated control settings, encompassing challenging manipulation and navigation tasks. Most notably, we show that Stable Control Representations enable learning policies that exhibit state-of-the-art performance on OVMM, a difficult open-vocabulary navigation benchmark.

Understanding Stable Control Representations for Embodied AI

The Challenge of Representations in Embodied AI

Embodied AI involves agents interacting within a physical environment. To operate effectively, these agents need to interpret their surroundings accurately, integrating visual and linguistic inputs. A popular approach has been to use pre-trained vision-LLMs like CLIP. However, such models often lack the specificity required for detailed environmental interaction, which is crucial in scenarios like robotic control where precision is vital.

Stable Control Representations (SCR)

Researchers have turned to text-to-image diffusion models as an alternative source of representations. These models, primarily designed for generating images from textual descriptions, encode rich visual and language cues into their process, capable of capturing nuanced details in images. By extracting what this paper calls Stable Control Representations from these pre-trained models, the paper offers a new method to learn control policies that can interpret complex scenes and interact appropriately.

Impressive Performance

SCRs were tested across several simulated control settings, showing promising results. Notably, SCRs performed well in an open-vocabulary navigation benchmark, excelling in scenarios involving unseen object categories, highlighting their generalization capability. The representations also facilitated superior policy learning compared to the state-of-the-art, especially in environments that required detailed scene understanding.

Insights and Innovations

Multi-Step Approach to Extract Representations

The method involves a nuanced multi-step process to harvest these representations from diffusion models:

• Layer Selection and Aggregation: The paper emphasizes the importance of strategic layer selection from the diffusion model to capture various scene details.
• Diffusion Timestep Selection: Different stages in the diffusion process were explored to optimize the quality of the extracted representations, balancing between image fidelity and abstraction.
• Incorporation of Text Prompts: Text prompts guide the image generation in diffusion models, potentially tailoring the output representations to better suit task-specific needs.

Future Implications and Considerations

Practical Application

For robotic control tasks and AI systems requiring interaction with physical environments, SCRs provide a robust framework for interpreting complex visual settings. This could enhance the effectiveness of AI in sectors like automated manufacturing, logistics, and even autonomous navigation.

Theoretical Enhancement

This approach pushes the boundary on using generative models for function-specific tasks and opens new avenues in research, particularly on fine-tuning generative processes for task-specific representations.

Future Development

The continued advancement in diffusion models and their interpretability will likely expand the utility of SCRs. Future investigations could focus on refining extraction techniques, improving training efficiency, and broadening application areas for this technology.

Conclusion

The utilization of Stable Control Representations extracted from text-to-image diffusion models marks a significant leap towards integrating fine-grained visual understanding in embodied AI systems. This advancement bridges the gap between general-purpose representation learning and practical, control-specific applications, paving the way for more intelligent and adaptable robotic systems. As these technologies evolve, we can anticipate more sophisticated interactions between AI and the physical world, leading to broader implementation and innovative applications.