AdaClearGrasp: 学习自适应清除以实现零样本鲁棒灵巧抓取在密集障碍环境中
AdaClearGrasp: Learning Adaptive Clearing for Zero-Shot Robust Dexterous Grasping in Densely Cluttered Environments
AI总结：
AdaClearGrasp通过自适应清除与零样本灵巧抓取技术，提升机器人在密集障碍环境中的抓取成功率。
摘要：
在密集障碍环境中，物理干扰、视觉遮挡和不稳定接触常常导致直接的灵巧抓取失败，而激进的分离策略可能会损害安全。因此，使机器人能够自适应地决定是否清除周围物体或直接抓取目标对于鲁棒操作至关重要。我们提出了AdaClearGrasp，一种闭环决策-执行框架，用于在密集障碍环境中实现自适应清除和零样本灵巧抓取。该框架将操作视为一个可控的高层决策过程，决定是否直接抓取目标或首先清除周围物体。一个预训练的视觉-语言模型（VLM）解释视觉观察和语言任务描述，以推断抓取干扰并生成高层规划骨架，该骨架通过统一的动作接口调用结构化的原子技能。对于灵巧抓取，我们训练了一个强化学习策略，使用相对手-物体距离表示，使该策略能够在各种物体几何形状和物理特性上实现零样本泛化。在执行过程中，视觉反馈监控结果，并在失败时触发重新规划，形成闭环修正机制。为了评估语言条件下的灵巧抓取在障碍物中的表现，我们引入了Clutter-Bench，这是第一个具有分级障碍物复杂度的模拟基准。它包括三个障碍级别中的七个目标物体，产生210个任务场景。我们进一步在三个物体上进行了模拟到现实的实验，三个障碍级别（18个场景）。结果表明，AdaClearGrasp显著提高了在密集障碍环境中的抓取成功率。更多视频和代码，请访问我们的项目网站：https://chenzixuan99.github.io/adaclear-grasp.github.io/。
英文摘要：
In densely cluttered environments, physical interference, visual occlusions, and unstable contacts often cause direct dexterous grasping to fail, while aggressive singulation strategies may compromise safety. Enabling robots to adaptively decide whether to clear surrounding objects or directly grasp the target is therefore crucial for robust manipulation. We propose AdaClearGrasp, a closed-loop decision-execution framework for adaptive clearing and zero-shot dexterous grasping in densely cluttered environments. The framework formulates manipulation as a controllable high-level decision process that determines whether to directly grasp the target or first clear surrounding objects. A pretrained vision-language model (VLM) interprets visual observations and language task descriptions to reason about grasp interference and generate a high-level planning skeleton, which invokes structured atomic skills through a unified action interface. For dexterous grasping, we train a reinforcement learning policy with a relative hand-object distance representation, enabling zero-shot generalization across diverse object geometries and physical properties. During execution, visual feedback monitors outcomes and triggers replanning upon failures, forming a closed-loop correction mechanism. To evaluate language-conditioned dexterous grasping in clutter, we introduce Clutter-Bench, the first simulation benchmark with graded clutter complexity. It includes seven target objects across three clutter levels, yielding 210 task scenarios. We further perform sim-to-real experiments on three objects under three clutter levels (18 scenarios). Results demonstrate that AdaClearGrasp significantly improves grasp success rates in densely cluttered environments. For more videos and code, please visit our project website: https://chenzixuan99.github.io/adaclear-grasp.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
