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DAHLIA advances robotic long-horizon manipulation by leveraging in-context code generation and episodic feedback, enabling LLMs to synthesize executable plans without dense step-wise supervision. This approach significantly improves generalization across complex environments like cluttered scenes while reducing computational overhead through post-episode evaluation rather than per-step inference. The method represents a notable shift toward data-agnostic frameworks that treat robotic planning as a structured code-generation problem guided by chain-of-thought reasoning.
Robotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback
arXiv:2503.21969v4 Announce Type: replace-cross Abstract: Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-conditioned frameworks often rely on dense step-wise feedback, learned action policies, or unstructured prompting, which limits robustness and generalization. We propose DAHLIA, a data-agnostic code-generation framework that treats long-horizon manipulation as episodic task planning and evaluation. DAHLIA uses progressively organized in-context examples with chain-of-thought reasoning to guide an LLM in synthesizing executable code plans from language instructions. To improve robustness under partial observability, a VLM-based reporter evaluates task outcomes only after each execution episode and provides structured feedback for replanning, avoiding the overhead of per-step inference. Experiments on LoHoRavens, CALVIN, Franka Kitchen, and real-world manipulation tasks show that DAHLIA achieves strong performance on over 30 long-horizon tasks and improves generalization to unseen scenarios, including cluttered scenes and occluded objects.
Abstract:Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-conditioned frameworks often rely on dense step-wise feedback, learned action policies, or unstructured prompting, which limits robustness and generalization. We propose DAHLIA, a data-agnostic code-generation framework that treats long-horizon manipulation as episodic task planning and evaluation. DAHLIA uses progressively organized in-context examples with chain-of-thought reasoning to guide an LLM in synthesizing executable code plans from language instructions. To improve robustness under partial observability, a VLM-based reporter evaluates task outcomes only after each execution episode and provides structured feedback for replanning, avoiding the overhead of per-step inference. Experiments on LoHoRavens, CALVIN, Franka Kitchen, and real-world manipulation tasks show that DAHLIA achieves strong performance on over 30 long-horizon tasks and improves generalization to unseen scenarios, including cluttered scenes and occluded objects.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2503.21969 [cs.RO]
(or arXiv:2503.21969v4 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2503.21969 Focus to learn more arXiv-issued DOI via DataCite
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