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.

翻译：本文针对仿人机器人的任务导向抓取问题展开研究，重点强调其需符合人类社会规范并满足特定任务目标。现有方法虽采用了多种开环与闭环策略，但缺乏能够同时抓取多个物体并兼顾下游任务约束的端到端解决方案。我们提出的方法利用强化学习来增强任务导向抓取能力，并优先考虑智能体的抓取后意图。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）训练手部协同模型以模仿参与者的抓取动作。基于此数据，我们训练出一个能够抓取多个物体、同时兼顾不同任务特定抓取后意图的智能体。通过将人类抓取行为的数据驱动洞见与强化学习提供的探索式学习相结合，我们能够开发出具备情境感知操作能力的仿人机器人，从而促进以人为中心环境中的协作。