Learning Universal Policies via Text-Guided Video Generation
(2302.00111)Abstract
A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impressive ability to generate complex novel images, exhibiting combinatorial generalization across domains. Motivated by this success, we investigate whether such tools can be used to construct more general-purpose agents. Specifically, we cast the sequential decision making problem as a text-conditioned video generation problem, where, given a text-encoded specification of a desired goal, a planner synthesizes a set of future frames depicting its planned actions in the future, after which control actions are extracted from the generated video. By leveraging text as the underlying goal specification, we are able to naturally and combinatorially generalize to novel goals. The proposed policy-as-video formulation can further represent environments with different state and action spaces in a unified space of images, which, for example, enables learning and generalization across a variety of robot manipulation tasks. Finally, by leveraging pretrained language embeddings and widely available videos from the internet, the approach enables knowledge transfer through predicting highly realistic video plans for real robots.
Overview
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This paper introduces a novel approach for creating general-purpose AI agents through text-guided video generation, aiming to bridge the gap between varied tasks and environments.
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The proposed model, UniPi, generates policies from videos created from textual task descriptions, enabling agents to understand and perform tasks in diverse settings.
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Key advantages include combinatorial generalization, multi-task learning, hierarchical planning and adaptability, and the use of internet-scale knowledge transfer.
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Empirical evaluations show UniPi's superior generalization across tasks and environments and its potential in real-world applications, setting a foundation for future general-purpose AI research.
Exploring the Frontiers of Agent Generalization through Text-Guided Video Generation
Introduction to Text-Guided Video Generation for Universal Policies
Recent advancements in AI have opened up innovative avenues for creating more adaptable and general-purpose agents. At the heart of these developments lies the ambition to bridge the gap between varied tasks and environments, leveraging the power of text-guided image and video synthesis. This study introduces a pioneering approach that combines the realms of decision making and generative AI, presenting a method where agents learn universal policies through text-conditioned video generation. By transforming sequential decision-making problems into video synthesis tasks guided by textual instructions, this research pioneers a path towards agents capable of operating across multifarious environments with unprecedented generalizability.
Universal Policy via Video Generation
At its core, the proposed model, termed UniPi, encodes policies within videos generated from textual descriptions of tasks. This paradigm shift leverages video as a universal representation medium, enabling agents to comprehend and execute tasks in diverse settings by simply observing synthetic video outcomes. The methodology involves generating a trajectory in the form of a video sequence from a given text description. Subsequently, an inverse dynamics model is employed to deduce the underlying actions from these videos, crafting a policy that aligns with the demonstrated behaviors.
Key Findings and Advantages
UniPi's novel approach yields several significant benefits:
- Combinatorial Generalization: By leveraging languages’ intrinsic combinatorial nature, UniPi synthesizes actions for novel task combinations unseen during training, indicating a profound level of generalization.
- Multi-Task Learning: The framework seamlessly learns across a broad spectrum of tasks, demonstrating robust generalization to new challenges without the need for task-specific tuning.
- Hierarchical Planning and Adaptability: The video generation process mirrors hierarchical planning, enabling granular and adaptable action sequencing. Furthermore, plans can be dynamically modified by imposing new constraints at test time, showcasing the model’s flexibility.
- Internet-Scale Knowledge Transfer: Pretraining on a vast corpus of internet videos endows UniPi with a wide range of "demonstrations," facilitating the synthesis of realistic plans for physical robots based on verbal instructions.
Experimental Insights
The empirical evaluation underscores UniPi's remarkable capacity to generalize across tasks and environments:
- Combinatorial Policy Synthesis: Across a variety of language-guided tasks, UniPi significantly outperforms contemporary models, especially in novel task combinations, demonstrating strong combinatorial generalization capabilities.
- Multi-Environment Transfer: In multi-task settings, UniPi exhibits exceptional transfer learning abilities, successfully applying knowledge gleaned from seen tasks to perform in novel environments.
- Real World Transfer: The method showcases promising results in translating synthesized plans to real-world robot actions, further proving its potential for practical applications.
Forward-Looking Perspectives
This research delineates a significant stride towards achieving general-purpose AI, propelling the field beyond environment-specific models towards agents with broad applicational scope. The integration of text-guided video synthesis into policy development not only enhances an agent’s ability to generalize across tasks but also paves the way for leveraging the vast expanse of visual data available online for learning complex behaviors.
Looking ahead, the amalgamation of UniPi with LLMs appears to be a promising horizon, potentially unlocking even more sophisticated generalization and adaptability in AI agents. Furthermore, addressing the current limitations, such as the computational intensity of video generation and the model's performance in partially observable environments, will be crucial in realizing the full potential of this innovative approach.
Conclusion
In summary, converting policy learning into a text-conditioned video synthesis task poses an exciting paradigm shift, promising agents capable of understanding and executing a diverse array of tasks based on textual instructions. This research not only broadens the horizons of what AI can achieve but also lays down foundational work for future exploration in the domain of general-purpose AI agents.
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