VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought

• URL: http://arxiv.org/abs/2406.14596v5
• Date: Mon, 20 Jan 2025 23:33:33 GMT
• Title: VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought
• Authors: Gabriel Sarch, Lawrence Jang, Michael J. Tarr, William W. Cohen, Kenneth Marino, Katerina Fragkiadaki,
• Abstract summary: ICAL iteratively refines suboptimal trajectories into high-quality data with optimized actions and detailed reasoning.<n>ICAL surpasses state-of-the-art in TEACh, VisualWebArena, and Ego4D.<n>ICAL scales 2x better than raw human demonstrations and reduces manual prompt engineering.
• Score: 38.03704123835915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale LLMs and VLMs excel at few-shot learning but require high-quality examples. We introduce In-Context Abstraction Learning (ICAL), which iteratively refines suboptimal trajectories into high-quality data with optimized actions and detailed reasoning. Given an inefficient demonstration, a VLM corrects actions and annotates causal relationships, object states, subgoals, and task-relevant visuals, forming "programs of thought." With human feedback, these programs are improved as the agent executes them in a similar environment. The resulting examples, used as prompt context or fine-tuning data, significantly boost decision-making while reducing human feedback needs. ICAL surpasses state-of-the-art in TEACh (dialogue-based instruction following), VisualWebArena (multimodal web agents), and Ego4D (egocentric video action anticipation). In TEACh, combining fine-tuning and retrieval on ICAL examples outperforms raw human demonstrations and expert examples, achieving a 17.5% increase in goal-condition success. In VisualWebArena, retrieval-augmented GPT-4V with ICAL improves task success rate 1.6x over GPT-4V, while fine-tuning Qwen2-VL achieves a 2.8x improvement. In Ego4D, ICAL outperforms few-shot GPT-4V and remains competitive with supervised models. Overall, ICAL scales 2x better than raw human demonstrations and reduces manual prompt engineering.

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