面向任务的灵巧机器人抓取：利用姿态协同与强化学习
Task-oriented grasping for dexterous robots using postural synergies and reinforcement learning
AI总结：
本文提出利用姿态协同和强化学习方法，使人形机器人能够根据任务需求抓取多个物体并适应不同情境。
摘要：
本文针对人形机器人面向任务的抓取问题，强调需要与人类社会规范和任务特定目标对齐。现有方法采用多种开环和闭环方法，但缺乏一个端到端的解决方案，能够抓取多个物体同时考虑下游任务的约束。我们提出的方法采用强化学习来增强面向任务的抓取，优先考虑代理抓取后的意图。我们从ContactPose数据集中提取人类抓取偏好，并基于变分自编码器（VAE）训练手协同模型以模仿参与者的抓取动作。基于此数据，我们训练一个能够抓取多个物体并考虑不同任务特定的抓取后意图的代理。通过结合来自人类抓取行为的数据驱动洞察与强化学习提供的探索学习，我们可以开发出能够进行情境感知操作的人形机器人，促进在以人为中心的环境中协作。
英文摘要：
In this paper, we address the problem of task-oriented grasping for humanoid robots, emphasizing the need to align with human social norms and task-specific objectives. Existing methods, employ a variety of open-loop and closed-loop approaches but lack an end-to-end solution that can grasp several objects while taking into account the downstream task's constraints. Our proposed approach employs reinforcement learning to enhance task-oriented grasping, prioritizing the post-grasp intention of the agent. We extract human grasp preferences from the ContactPose dataset, and train a hand synergy model based on the Variational Autoencoder (VAE) to imitate the participant's grasping actions. Based on this data, we train an agent able to grasp multiple objects while taking into account distinct post-grasp intentions that are task-specific. By combining data-driven insights from human grasping behavior with learning by exploration provided by reinforcement learning, we can develop humanoid robots capable of context-aware manipulation actions, facilitating collaboration in human-centered environments.
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。
