从单次演示中学习自适应灵巧抓取
Learning Adaptive Dexterous Grasping from Single Demonstrations
AI总结：
提出 AdaDexGrasp 框架，通过单次演示学习抓取技能库并利用 VLM 进行上下文选择，结合轨迹跟随奖励与课程学习提升 RL 效率，实现零样本真实世界迁移且成功率达 90%。
摘要：
机器人如何高效学习灵巧抓取技能并根据用户指令自适应地应用它们？本工作解决了两个关键挑战：从有限的人类演示中高效获取技能以及基于上下文的技能选择。我们提出了 AdaDexGrasp 框架，该框架从每种技能的单次人类演示中学习抓取技能库，并使用视觉语言模型（VLM）选择最合适的技能。为提高样本效率，我们提出了一种轨迹跟随奖励，引导强化学习（RL）向接近人类演示的状态推进，同时允许探索的灵活性。为在单次演示之外进行学习，我们采用课程学习，逐步增加物体位姿变化以增强鲁棒性。在部署时，VLM 根据用户指令检索合适的技能，将低级学习技能与高级意图连接起来。我们在仿真和真实世界环境中评估了 AdaDexGrasp，结果表明我们的方法显著提高了 RL 效率，并能在不同物体配置下学习类人抓取策略。最后，我们展示了学习到的策略零样本迁移到真实世界的 PSYONIC Ability Hand，在物体上的成功率达 90%，显著优于基线。
英文摘要：
How can robots learn dexterous grasping skills efficiently and apply them adaptively based on user instructions? This work tackles two key challenges: efficient skill acquisition from limited human demonstrations and context-driven skill selection. We introduce AdaDexGrasp, a framework that learns a library of grasping skills from a single human demonstration per skill and selects the most suitable one using a vision-language model (VLM). To improve sample efficiency, we propose a trajectory following reward that guides reinforcement learning (RL) toward states close to a human demonstration while allowing flexibility in exploration. To learn beyond the single demonstration, we employ curriculum learning, progressively increasing object pose variations to enhance robustness. At deployment, a VLM retrieves the appropriate skill based on user instructions, bridging low-level learned skills with high-level intent. We evaluate AdaDexGrasp in both simulation and real-world settings, showing that our approach significantly improves RL efficiency and enables learning human-like grasp strategies across varied object configurations. Finally, we demonstrate zero-shot transfer of our learned policies to a real-world PSYONIC Ability Hand, with a 90% success rate across objects, significantly outperforming the baseline.
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。
