← 研踪 PaperTrailarXiv:2611.16671 · v1
NSDL: Neuro-Symbolic Division of Labor for Multi-Robot Task Planning
首次提交 2026-11-12 19:47
版本更新 2026-11-12 19:47
雷达观测 2026-11-13 14:15
摘要
Planning for a heterogeneous robot team from a scene image and an instruction involves two kinds of decisions. Some must be judged from the scene: which goals are already met, how the task splits, and which robot does what. Others follow from these and can be computed: prerequisite actions, handoffs between robots, and timing. We argue that each kind should go to the component suited to it: a small vision-language model learns the first, and a deterministic solver computes the second. In NSDL (Neuro-Symbolic Division of Labor), the trained model writes its judgments as a plan sketch, which lists the goals that still need action and the intended steps in order, each with a robot. The solver expands the sketch into an executable joint plan. The model calls two tools, a detector for evidence and the solver, and is trained with the solver in the loop. We evaluate on the public VIKI-L2 benchmark and on LOCUS-Bench, a new benchmark that grades the two kinds of difficulty separately. NSDL outperforms end-to-end planners and formalize-then-plan baselines on both benchmarks and with three backbones from 3B to 8B; the 7B model reaches 97.0% out-of-distribution accuracy on VIKI-L2. Ordering and scheduling errors nearly disappear once the solver writes the structure; the remaining failures are misjudged states, and most goals the model misses are ones it never lists. Code, model weights, and LOCUS-Bench are publicly available at https://github.com/Xincheng-Tan/NSDL.
作者备注：10 pages, 3 figures, 5 tables. Submitted to ICRA 2027
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v1 · 2026-11-12 19:47 · 最新观测版本
NSDL: Neuro-Symbolic Division of Labor for Multi-Robot Task Planning
Planning for a heterogeneous robot team from a scene image and an instruction involves two kinds of decisions. Some must be judged from the scene: which goals are already met, how the task splits, and which robot does what. Others follow from these and can be computed: prerequisite actions, handoffs between robots, and timing. We argue that each kind should go to the component suited to it: a small vision-language model learns the first, and a deterministic solver computes the second. In NSDL (Neuro-Symbolic Division of Labor), the trained model writes its judgments as a plan sketch, which lists the goals that still need action and the intended steps in order, each with a robot. The solver expands the sketch into an executable joint plan. The model calls two tools, a detector for evidence and the solver, and is trained with the solver in the loop. We evaluate on the public VIKI-L2 benchmark and on LOCUS-Bench, a new benchmark that grades the two kinds of difficulty separately. NSDL outperforms end-to-end planners and formalize-then-plan baselines on both benchmarks and with three backbones from 3B to 8B; the 7B model reaches 97.0% out-of-distribution accuracy on VIKI-L2. Ordering and scheduling errors nearly disappear once the solver writes the structure; the remaining failures are misjudged states, and most goals the model misses are ones it never lists. Code, model weights, and LOCUS-Bench are publicly available at https://github.com/Xincheng-Tan/NSDL.
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