神经符号方法在长时序操作任务中胜过视觉语言动作模型，且能耗更低。
The Price Is Not Right: Neuro-Symbolic Methods Outperform VLAs on Structured Long-Horizon Manipulation Tasks with Significantly Lower Energy Consumption
- 用符号规划+学习控制的神经符号架构替代端到端VLA
- 3块塔任务成功率95%对34%，4块新任务也成功78%
- 训练能耗比VLA低近两个数量级，适合高效可靠机器人系统
视觉语言动作（VLA）模型被视作实现通用机器人策略的路径，能解析自然语言和视觉输入以生成操作动作。然而其在结构化、长时序操作任务中的有效性与效率尚不明确。本文通过实证比较微调的开源VLA模型π0与结合PDDL符号规划与学习低层控制的神经符号架构。在仿真环境中评估二者在结构化汉诺塔任务上的表现，并测量训练与执行阶段的能量消耗。在3块塔任务中，神经符号模型成功率达95%，而最佳VLA仅为34%；神经符号模型还能泛化至未见过的4块塔任务（成功78%），而所有VLA均失败。训练阶段，VLA微调能耗接近神经符号方法的100倍。结果凸显了端到端基础模型与结构化推理架构在长时序机器人操作中的权衡，强调显式符号结构对提升可靠性、数据效率与能源效率的关键作用。代码与模型见https://price-is-not-right.github.io
原文摘要 · Abstract (English)
Vision-Language-Action (VLA) models have recently been proposed as a pathway toward generalist robotic policies capable of interpreting natural language and visual inputs to generate manipulation actions. However, their effectiveness and efficiency on structured, long-horizon manipulation tasks remain unclear. In this work, we present a head-to-head empirical comparison between a fine-tuned open-weight VLA model π0 and a neuro-symbolic architecture that combines PDDL-based symbolic planning with learned low-level control. We evaluate both approaches on structured variants of the Towers of Hanoi manipulation task in simulation while measuring both task performance and energy consumption during training and execution. On the 3-block task, the neuro-symbolic model achieves 95% success compared to 34% for the best-performing VLA. The neuro-symbolic model also generalizes to an unseen 4-block variant (78% success), whereas both VLAs fail to complete the task. During training, VLA fine-tuning consumes nearly two orders of magnitude more energy than the neuro-symbolic approach. These results highlight important trade-offs between end-to-end foundation-model approaches and structured reasoning architectures for long-horizon robotic manipulation, emphasizing the role of explicit symbolic structure in improving reliability, data efficiency, and energy efficiency. Code and models are available at https://price-is-not-right.github.io
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