在密集杂乱环境中学习双臂推抓协同
Learning Dual-Arm Push and Grasp Synergy in Dense Clutter
AI总结：
提出面向目标的分层DRL框架学习双臂推抓协同，结合预训练主干、CNN模型、PPO算法与模糊奖励函数，实现6-DoF抓取与双臂推动作，高效完成密集杂乱环境中的抓取任务。
摘要：
由于缺乏无碰撞的抓取可供性，密集杂乱环境中的机器人抓取极具挑战性。非抓持动作能增加杂乱环境中的可行抓取，但现有研究多集中于单臂而非双臂操作。单臂系统的策略无法充分发挥双臂协同的优势。我们提出一种面向目标的分层深度强化学习（DRL）框架，通过学习双臂推抓协同来抓取物体，以提升密集杂乱环境中的灵巧操作。该框架通过预训练深度学习主干与基于CNN的新型DRL模型，利用Proximal Policy Optimization（PPO）训练，将视觉观测映射为动作以制定双臂推抓策略。主干网络增强了密集杂乱环境中的特征映射。引入新型模糊奖励函数加速策略学习。系统在Isaac Gym中开发训练，并在仿真与真实机器人上测试。实验表明，该框架能将视觉数据映射为双臂推抓运动，使系统在复杂环境中抓取目标。与其他方法相比，本方法生成6-DoF抓取候选并实现双臂推动作，模拟人类行为。结果显示该方法能高效完成密集杂乱环境中的任务。
英文摘要：
Robotic grasping in densely cluttered environments is challenging due to scarce collision-free grasp affordances. Non-prehensile actions can increase feasible grasps in cluttered environments, but most research focuses on single-arm rather than dual-arm manipulation. Policies from single-arm systems fail to fully leverage the advantages of dual-arm coordination. We propose a target-oriented hierarchical deep reinforcement learning (DRL) framework that learns dual-arm push-grasp synergy for grasping objects to enhance dexterous manipulation in dense clutter. Our framework maps visual observations to actions via a pre-trained deep learning backbone and a novel CNN-based DRL model, trained with Proximal Policy Optimization (PPO), to develop a dual-arm push-grasp strategy. The backbone enhances feature mapping in densely cluttered environments. A novel fuzzy-based reward function is introduced to accelerate efficient strategy learning. Our system is developed and trained in Isaac Gym and then tested in simulations and on a real robot. Experimental results show that our framework effectively maps visual data to dual push-grasp motions, enabling the dual-arm system to grasp target objects in complex environments. Compared to other methods, our approach generates 6-DoF grasp candidates and enables dual-arm push actions, mimicking human behavior. Results show that our method efficiently completes tasks in densely cluttered environments. https://sites.google.com/view/pg4da/home
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。
