affordanceGrasp-R1: 利用基于推理的 affordance 分割与强化学习进行机器人抓取
AffordanceGrasp-R1:Leveraging Reasoning-Based Affordance Segmentation with Reinforcement Learning for Robotic Grasping
AI总结：
AffordanceGrasp-R1 通过结合推理和强化学习，提升机器人抓取任务中的推理能力和空间定位，实验表明其在基准和现实场景中均表现优异。
摘要：
我们介绍了 AffordanceGrasp-R1，一种基于推理的 affordance 分割框架，用于机器人抓取，该框架结合了链式思维（CoT）冷启动策略与强化学习以增强推理和空间定位。此外，我们重新设计抓取流程，使其更加上下文感知，通过从全局场景点云生成抓取候选，并随后使用指令条件的 affordance 隐藏层进行过滤。广泛的实验表明，AffordanceGrasp-R1 在基准数据集上始终优于最先进的（SOTA）方法，而现实世界的机器人抓取评估进一步验证了其在复杂语言条件操控场景下的鲁棒性和泛化能力。
英文摘要：
We introduce AffordanceGrasp-R1, a reasoning-driven affordance segmentation framework for robotic grasping that combines a chain-of-thought (CoT) cold-start strategy with reinforcement learning to enhance deduction and spatial grounding. In addition, we redesign the grasping pipeline to be more context-aware by generating grasp candidates from the global scene point cloud and subsequently filtering them using instruction-conditioned affordance masks. Extensive experiments demonstrate that AffordanceGrasp-R1 consistently outperforms state-of-the-art (SOTA) methods on benchmark datasets, and real-world robotic grasping evaluations further validate its robustness and generalization under complex language-conditioned manipulation scenarios.
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。
