ALFWorld: Aligning Text and Embodied Environments for Interactive Learning (2010.03768v2)

Abstract: Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to fit the scene. Embodied agents require the same abilities, but existing work does not yet provide the infrastructure necessary for both reasoning abstractly and executing concretely. We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstract, text based policies in TextWorld (C^ot\'e et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment. ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions. In turn, as we demonstrate empirically, this fosters better agent generalization than training only in the visually grounded environment. BUTLER's simple, modular design factors the problem to allow researchers to focus on models for improving every piece of the pipeline (language understanding, planning, navigation, and visual scene understanding).

• The paper introduces ALFWorld, a novel framework that aligns text-based reasoning with embodied execution for interactive AI learning.
• The BUTLER agent leverages imitation learning in TextWorld and successfully transfers policies to ALFRED’s environment to enhance performance.
• Experimental results highlight 7x faster training in text simulations and notable improvements in zero-shot generalization for embodied tasks.

Overview of "ALFWorld: Aligning Text and Embodied Environments for Interactive Learning"

The paper "ALFWorld: Aligning Text and Embodied Environments for Interactive Learning" addresses a significant challenge in AI, particularly in the domain of embodied AI agents. The authors introduce ALFWorld, a novel simulation framework that integrates text-based and embodied environments to enhance the interactive learning capabilities of AI agents. This integration allows for the abstraction in planning while maintaining the concreteness required for execution. It is a progressive effort merging two distinct platforms: TextWorld—a text-based interactive learning platform, and ALFRED—a visually rich embodied AI benchmark.

Key Contributions

• ALFWorld Framework: The paper's core contribution is the creation of ALFWorld, which aligns the abstract reasoning possible in text-based environments with the execution demands of physically embodied environments. This cross-modal integration allows AI agents to perform high-level reasoning in a text-based simulation before applying the learned policies in a physically simulated world.
• BUTLER Agent: The authors introduce the BUTLER agent, designed to operate within the ALFWorld framework. This agent learns abstract tasks using imitation learning in TextWorld and subsequently applies these abstract policies to complete embodied tasks in ALFRED's environment. BUTLER demonstrates improved generalization capabilities as compared to agents trained in isolation within visually grounded environments.
• Experimental Results: The empirical evaluations presented underline the efficacy of ALFWorld in facilitating better training efficiency and generalization performance. Training in TextWorld is found to be seven times faster than solely within the embodied environment, and it yields superior performance. Specifically, the transfer of learned policies from TextWorld to ALFRED is notably effective, substantially impacting the agents' zero-shot generalization capabilities.

Implications and Future Directions

The development of ALFWorld represents a strategic advancement in embodied AI research, with significant implications for both practical applications and theoretical advancements. The ability to pre-train embodied agents in an abstract textual space addresses a gap in the field where physical embodiment and interaction are costly and slow processes.

In practical terms, the modularity of the BUTLER agent suggests pathways for incremental improvements in individual components such as language understanding, planning, navigation, and visual scene comprehension. This modular design fosters collaboration and targeted advancements in specific AI capabilities without necessitating a complete overhaul of the system.

Looking forward, ALFWorld sets a new precedent for artificial environments used in AI research. It opens avenues for more comprehensive systems where text-based scenarios simulate potential real-world interactions, minimizing the need for expensive real-world data collection. The framework also paves the way for the development of more robust AI systems capable of understanding and acting upon high-level instructions in unfamiliar environments. Additionally, further research could focus on softening the domain gap between text-based simulations and embodied environments to improve real-world application potential.

In conclusion, ALFWorld is a significant academic contribution towards enhancing interactive learning capabilities in AI agents, bridging the gap between abstract reasoning and practical execution. This paper significantly advances the field by proposing an integrated framework that marries the efficiency of text-based learning with the realism of embodied tasks.