UltraDexGrasp: 为双臂机器人学习通用灵巧抓取
UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data
AI总结：
UltraDexGrasp通过合成数据训练出高效抓取策略，实现双臂机器人在多种物体上的高成功率抓取。
摘要：
抓取是机器人与物理世界交互的基本能力。人类凭借双手，能够根据物体的形状、大小和重量自主选择合适的抓取策略，实现稳健的抓取和后续操作。相比之下，当前机器人的抓取能力仍然有限，特别是在多策略设置中。尽管已有大量研究针对并行抓手和单手抓取，但双臂机器人的灵巧抓取仍处于探索阶段，数据成为主要瓶颈。实现物理合理且几何符合的抓取，能够承受外部力矩，具有重大挑战。为了解决这些问题，我们引入UltraDexGrasp，为双臂机器人提供通用灵巧抓取的框架。所提出的数据生成管道整合了基于优化的抓取合成与基于规划的演示生成，产生多种抓取策略的高质量和多样化的轨迹。借助该框架，我们整理了UltraDexGrasp-20M，一个包含2000万个帧、覆盖1000个物体的大型多策略抓取数据集。基于UltraDexGrasp-20M，我们进一步开发了一种简单而有效的抓取策略，输入点云，通过单向注意力聚合场景特征，并预测控制命令。该策略仅在合成数据上训练，实现了稳健的零样本仿真到现实迁移，并在各种形状、大小和重量的新型物体上持续成功，平均成功率为81.2%。为了促进未来双臂机器人抓取研究，我们开源了数据生成管道，网址为https://github.com/InternRobotics/UltraDexGrasp。
英文摘要：
Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape, size, and weight of objects, enabling robust grasping and subsequent manipulation. In contrast, current robotic grasping remains limited, particularly in multi-strategy settings. Although substantial efforts have targeted parallel-gripper and single-hand grasping, dexterous grasping for bimanual robots remains underexplored, with data being a primary bottleneck. Achieving physically plausible and geometrically conforming grasps that can withstand external wrenches poses significant challenges. To address these issues, we introduce UltraDexGrasp, a framework for universal dexterous grasping with bimanual robots. The proposed data-generation pipeline integrates optimization-based grasp synthesis with planning-based demonstration generation, yielding high-quality and diverse trajectories across multiple grasp strategies. With this framework, we curate UltraDexGrasp-20M, a large-scale, multi-strategy grasp dataset comprising 20 million frames across 1,000 objects. Based on UltraDexGrasp-20M, we further develop a simple yet effective grasp policy that takes point clouds as input, aggregates scene features via unidirectional attention, and predicts control commands. Trained exclusively on synthetic data, the policy achieves robust zero-shot sim-to-real transfer and consistently succeeds on novel objects with varied shapes, sizes, and weights, attaining an average success rate of 81.2% in real-world universal dexterous grasping. To facilitate future research on grasping with bimanual robots, we open-source the data generation pipeline at https://github.com/InternRobotics/UltraDexGrasp.
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。
