让机器人像人一样抓取物体，还能根据任务调整抓握姿势。
Task-oriented grasping for dexterous robots using postural synergies and reinforcement learning
- 用变分自编码器学习人类抓握习惯，生成自然的抓握姿态。
- 结合强化学习，实现多物体抓取并适应不同任务需求。
- 适合需要与人协作的复杂操作场景，如家庭服务机器人。
本文研究人形机器人在任务导向抓取中的行为优化，强调需符合人类社会规范和具体任务目标。现有方法多采用开环或闭环策略，缺乏能同时处理多种物体且兼顾下游任务约束的端到端方案。本文提出一种基于强化学习的方法，优先考虑抓取后的动作意图。通过分析ContactPose数据集提取人类抓握偏好，利用变分自编码器（VAE）训练手部协同模型以模仿真实抓握行为。在此基础上，训练智能体实现对多个物体的抓取，并能根据任务特性调整抓握姿态。融合人类行为数据与强化学习的探索能力，使机器人具备上下文感知的灵巧操作能力，适用于以人为中心的人机协作环境。
原文摘要 · Abstract (English)
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.
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