基于势引导的粒子转向的否定约束灵巧抓取
Potential-Guided Particle Steering for Negation-Constrained Dexterous Grasping
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- Chung-Ang University（中央大学）
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中文总结 AI 辅助
本文针对语言驱动灵巧抓取模型无法处理否定约束的问题，提出无需否定训练示例的推理框架，在NegGrasp基准上显著降低了抓取违反率并提升了成功率。
中文摘要 AI 辅助
语言驱动的灵巧抓取模型（如DextER）在指令指定抓取位置时表现良好，但研究发现当指令同时指定禁止抓取的位置（例如“握住把手但避开主体”）时，这类模型会出现系统性失败。现有训练语料库（包括DexGYSNet）几乎没有包含否定指令，为每种可能的约束收集示例并不现实；此外，由于训练过程中提到的每个部位都被视为接触目标，模型可能将禁止部位解读为另一个抓取区域而非需要避开的区域。因此，本文提出一种无需否定特定训练示例的推理时否定约束灵巧抓取框架，将序贯蒙特卡洛与无分类器引导相结合，在训练过程中无需任何否定示例的情况下，引导采样指向指令指定部位，同时修剪朝向禁止区域的候选。一个冻结的3D部件 grounding 模型从语言指令中定位禁止区域。为评估该设置，本文构建了NegGrasp基准，该基准包含成对的正/负指令以及约束感知指标，仅当抓取既完成任务又符合指定约束时才判定为成功。在NegGrasp上，本文方法将最强基线的违反率从57.9%降至17.2%，同时提升了约束感知成功率和物理成功率。
英文摘要
Language-driven dexterous grasp models, such as DextER, perform well when instructions specify where to grasp, but we find they fail systematically when an instruction also specifies where not to grasp (e.g., "grasp the handle but avoid the body"). Existing training corpora, DexGYSNet among them, contain virtually no avoidance instructions, and collecting examples for every possible constraint is impractical. Moreover, because every part mentioned during training denotes a contact target, models may interpret a forbidden part as another region to grasp rather than one to avoid. We therefore introduce an inference-time framework for negation-constrained dexterous grasping that requires no negation-specific training examples. Combining Sequential Monte Carlo with classifier-free guidance, our method guides sampling toward the instructed part while pruning candidates headed for the forbidden region, without any negation examples during training. A frozen 3D part-grounding model localizes the forbidden region from the language instruction. To evaluate this setting, we construct NegGrasp, a benchmark of paired positive/negative instructions with constraint-aware metrics that credit a grasp only if it both accomplishes the task and respects the stated constraint. On NegGrasp, our method reduces the violation rate of the strongest baseline from 57.9% to 17.2% while improving both constraint-aware and physical success.
