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Learning with Language-Guided State Abstractions (2402.18759v2)

Published 28 Feb 2024 in cs.RO, cs.AI, and cs.LG

Abstract: We describe a framework for using natural language to design state abstractions for imitation learning. Generalizable policy learning in high-dimensional observation spaces is facilitated by well-designed state representations, which can surface important features of an environment and hide irrelevant ones. These state representations are typically manually specified, or derived from other labor-intensive labeling procedures. Our method, LGA (language-guided abstraction), uses a combination of natural language supervision and background knowledge from LLMs (LMs) to automatically build state representations tailored to unseen tasks. In LGA, a user first provides a (possibly incomplete) description of a target task in natural language; next, a pre-trained LM translates this task description into a state abstraction function that masks out irrelevant features; finally, an imitation policy is trained using a small number of demonstrations and LGA-generated abstract states. Experiments on simulated robotic tasks show that LGA yields state abstractions similar to those designed by humans, but in a fraction of the time, and that these abstractions improve generalization and robustness in the presence of spurious correlations and ambiguous specifications. We illustrate the utility of the learned abstractions on mobile manipulation tasks with a Spot robot.

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Authors (7)
  1. Andi Peng (17 papers)
  2. Ilia Sucholutsky (45 papers)
  3. Belinda Z. Li (21 papers)
  4. Theodore R. Sumers (16 papers)
  5. Thomas L. Griffiths (150 papers)
  6. Jacob Andreas (116 papers)
  7. Julie A. Shah (20 papers)
Citations (11)
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Summary

Overview of "Learning with Language-Guided State Abstractions"

The paper "Learning with Language-Guided State Abstractions" presents a novel framework, termed Language-Guided Abstraction (LGA), for designing state abstractions in imitation learning using natural language. This framework addresses a critical challenge in policy learning, especially in high-dimensional observation spaces where generalization is difficult and traditional state representation methods are labor-intensive.

In essence, LGA harnesses the expressive power of LLMs (LMs) to automatically generate state representations that emphasize relevant features while neglecting irrelevant ones. This automation aims to replicate the effectiveness of human-designed abstractions but at a fraction of the time. The method involves translating a natural language task description provided by a user into a function that masks irrelevant state features, subsequently using a limited set of demonstrations to train an imitation policy over these language-guided abstract states.

Key Contributions

  1. Conceptual Framework: The authors propose the conceptual workflow of LGA, beginning with a potentially incomplete natural language task description. A pre-trained LM then interprets this task description to derive a state abstraction function, isolating features significant to the stated task. This function facilitates policy learning using just a handful of demonstrations, by focusing purely on the generated abstract states.
  2. Experimental Validation: The paper provides empirical evidence from simulated robotic tasks demonstrating that LGA can derive state abstractions closely resembling those crafted by human designers. This resemblance is achieved with considerable efficiency gains. The experiments cover mobile manipulation tasks using a Spot robot, illustrating LGA's practical applicability in real-world settings.
  3. Robustness and Generalization: Through the experiments, it is evident that the state abstractions generated by LGA improve policy generalization and robustness amidst spurious correlations and ambiguous specifications. The framework effectively benefits from using both the intrinsic common-sense knowledge embedded in LMs and the specificity of natural language inputs to handle these challenges.
  4. Human and LM Synergy: While LGA is autonomous, an extension of this framework, LGA-HILL (Human In-the-Loop), allows human users to refine the state abstraction, providing room for personalization and domain-specific expertise to fine-tune the representations.

Implications

The implications of integrating LLMs into imitation learning frameworks like LGA are twofold. Practically, this approach could significantly reduce the overhead associated with designing feature-rich state representations manually, accelerating the deployment of imitation learning systems in complex, high-dimensional environments. Theoretically, it bridges the gap between linguistic comprehension and action-oriented representation learning, showcasing a versatile application of LMs beyond traditional NLP tasks.

This work also posits potential future research directions in developing more sophisticated abstraction mechanisms that may incorporate not only visual features but also contextual trajectory data, thereby enriching the representational robustness of the learning systems.

Future Developments

Future developments in this sphere could involve expanding the framework to handle multi-modal inputs and outputs. Additionally, refining the interaction between human input and machine-generated abstractions could further personalize and optimize learning outcomes. Another exciting avenue could involve enhancing the abstraction capabilities to autonomously recognize and incorporate non-visual but task-relevant cues, paving the way for more intelligent, context-aware learning systems.

In conclusion, LGA deftly illustrates how natural language can serve as a powerful tool in shaping more intuitive, efficient machine learning frameworks, thereby pushing the boundaries of what autonomous systems can achieve through naturalistic human-machine interaction.

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