Embodied Long Horizon Manipulation with Closed-loop Code Generation and Incremental Few-shot Adaptation

• URL: http://arxiv.org/abs/2503.21969v3
• Date: Thu, 21 Aug 2025 02:16:40 GMT
• Title: Embodied Long Horizon Manipulation with Closed-loop Code Generation and Incremental Few-shot Adaptation
• Authors: Yuan Meng, Xiangtong Yao, Haihui Ye, Yirui Zhou, Shengqiang Zhang, Zhenguo Sun, Xukun Li, Zhenshan Bing, Alois Knoll,
• Abstract summary: Embodied long-horizon manipulation requires robotic systems to process multimodal inputs-such as vision and natural language-and translate them into executable actions.<n>Recent methods have explored using large language models (LLMs) as high-level planners that decompose tasks into subtasks using natural language and guide pretrained low-level controllers.<n>Our framework achieves state-of-the-art performance on 30+ diverse seen and unseen long-horizon tasks across LoHoRavens, CALVIN, Franka Kitchen, and cluttered real-world settings.
• Score: 12.077740860502878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embodied long-horizon manipulation requires robotic systems to process multimodal inputs-such as vision and natural language-and translate them into executable actions. However, existing learning-based approaches often depend on large, task-specific datasets and struggle to generalize to unseen scenarios. Recent methods have explored using large language models (LLMs) as high-level planners that decompose tasks into subtasks using natural language and guide pretrained low-level controllers. Yet, these approaches assume perfect execution from low-level policies, which is unrealistic in real-world environments with noise or suboptimal behaviors. To overcome this, we fully discard the pretrained low-level policy and instead use the LLM to directly generate executable code plans within a closed-loop framework. Our planner employs chain-of-thought (CoT)-guided few-shot learning with incrementally structured examples to produce robust and generalizable task plans. Complementing this, a reporter evaluates outcomes using RGB-D and delivers structured feedback, enabling recovery from misalignment and replanning under partial observability. This design eliminates per-step inference, reduces computational overhead, and limits error accumulation that was observed in previous methods. Our framework achieves state-of-the-art performance on 30+ diverse seen and unseen long-horizon tasks across LoHoRavens, CALVIN, Franka Kitchen, and cluttered real-world settings.

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