Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few Examples

Abstract: Learning a perception and reasoning module for robotic assistants to plan steps to perform complex tasks based on natural language instructions often requires large free-form language annotations, especially for short high-level instructions. To reduce the cost of annotation, LLMs are used as a planner with few data. However, when elaborating the steps, even the state-of-the-art planner that uses LLMs mostly relies on linguistic common sense, often neglecting the status of the environment at command reception, resulting in inappropriate plans. To generate plans grounded in the environment, we propose FLARE (Few-shot Language with environmental Adaptive Replanning Embodied agent), which improves task planning using both language command and environmental perception. As language instructions often contain ambiguities or incorrect expressions, we additionally propose to correct the mistakes using visual cues from the agent. The proposed scheme allows us to use a few language pairs thanks to the visual cues and outperforms state-of-the-art approaches. Our code is available at https://github.com/snumprlab/flare.

• The paper introduces a multi-modal planner that fuses visual and textual inputs to enhance task planning with limited training examples.
• It proposes an environment adaptive replanning module that dynamically corrects actions without heavy reliance on large language models.
• The method achieves superior performance on the ALFRED benchmark, demonstrating significant gains in few-shot learning scenarios for embodied agents.

Overview of Multi-Modal Grounded Planning and Efficient Replanning for Learning Embodied Agents with A Few Examples

This paper introduces a novel approach to task planning for embodied agents using multi-modal inputs, specifically integrating visual environmental context with textual instructions. The authors propose a methodology called Multi-Modal Grounded Planning and Efficient Replanning (MMP) that aims to improve the ability of embodied agents—robots or virtual assistants that interact with their environments—to generate appropriate action plans even with limited training data.

Key Contributions

The study highlights the following primary contributions:

1. Multi-Modal Planner: The proposed system integrates both visual and textual input to form a more complete understanding of the task at hand. By weighing the similarity between current environmental conditions and training examples through both visual and textual features, this approach allows for better selection of task-relevant examples, which is pivotal when using LLMs for generating detailed action plans.
2. Environment Adaptive Replanning: To address the common issue of non-grounded plans caused by the diversity and ambiguity of language instructions, the authors implement a mechanism for partial correction of plans without recurring to an LLM, enhancing efficiency. This module allows the agents to dynamically adapt to the resources available in their immediate environment, recognizing when a planned action is possible or not and adjusting accordingly.
3. Competitive Performance in Few-Shot Learning: The methodology significantly outperforms comparable models on the ALFRED benchmark, achieving substantial improvements in task success rates even with minimal annotation data. The system demonstrates effectiveness in generalizing from a small set of examples due to its reliance on sophisticated planning and replanning strategies that integrate multi-modal data.

Experimental Evaluation

The research utilizes the ALFRED benchmark, which is a standard test for embodied task planning and execution, demonstrating that the proposed system achieves a notable increase in success rates compared to existing methods. The evaluation shows that the incorporation of environmental awareness and efficient replanning leads to substantial improvements, especially in tasks where precise navigation and interaction with specific objects are required.

The authors also explore various LLMs, including proprietary options like GPT-3.5 and GPT-4, and open-source models like LLaMA2 and Vicuna, to assess their framework's generalizability and effectiveness.

Implications and Speculations for Future AI Developments

This paper's findings suggest several implications for the future development of AI systems, particularly those involving embodied interactions:

• Enhanced Interaction Capabilities: The integration of multi-modal inputs leads to a richer understanding of tasks and environments, offering the potential for more sophisticated and autonomous agent interactions in varied settings.
• Improved Data-Efficiency: Demonstrating robust performance in data-scarce scenarios underscores the potential for deploying adaptable AI systems in real-world applications with minimal training data, reducing the overhead associated with dataset compilation.
• Potential for Broader Application: While the experimental focus is on household tasks, the principles of multi-modal grounded planning could extend to other domains, including industrial automation, healthcare, and customer service robots, where contextual understanding is critical.

Conclusion

The paper makes a significant step forward in the field of embodied AI by presenting a system that bridges the gap between language-based task instructions and environmental execution requirements. By effectively employing multi-modal grounding and efficient replanning, the proposed framework enhances the adaptability and efficiency of embodied agents, marking a noteworthy advancement in designing practical AI systems for complex and dynamic environments. Future research could further explore automated environment learning, potentially eliminating the need for pre-collected training data and thereby paving the way towards entirely self-sufficient intelligent systems.