Dexterous manipulation, particularly adept coordinating and grasping, constitutes a fundamental and indispensable capability for robots, facilitating the emulation of human-like behaviors. Integrating this capability into robots empowers them to supplement and even supplant humans in undertaking increasingly intricate tasks in both daily life and industrial settings. Unfortunately, contemporary methodologies encounter serious challenges in devising manipulation trajectories owing to the intricacies of tasks, the expansive robotic manipulation space, and dynamic obstacles. We propose a novel approach, APEX, to address all these difficulties by introducing a collision-free latent diffusion model for both robotic motion planning and manipulation. Firstly, we simplify the complexity of real-life ambidextrous dual-arm robotic manipulation tasks by abstracting them as aligning two vectors. Secondly, we devise latent diffusion models to produce a variety of robotic manipulation trajectories. Furthermore, we integrate obstacle information utilizing a classifier-guidance technique, thereby guaranteeing both the feasibility and safety of the generated manipulation trajectories. Lastly, we validate our proposed algorithm through extensive experiments conducted on the hardware platform of ambidextrous dual-arm robots. Our algorithm consistently generates successful and seamless trajectories across diverse tasks, surpassing conventional robotic motion planning algorithms. These results carry significant implications for the future design of diffusion robots, enhancing their capability to tackle more intricate robotic manipulation tasks with increased efficiency and safety. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/apex-dual-arm/home.

翻译：摘要：灵巧操作，尤其是熟练的协调与抓取能力，是机器人实现类人行为模拟的基础且不可或缺的能力。将这一能力赋予机器人，使其能够辅助甚至替代人类，在日常生活和工业场景中执行日益复杂的任务。然而，当前方法由于任务复杂性、机器人操作空间庞大以及动态障碍物的存在，在规划操作轨迹时面临严峻挑战。我们提出了一种名为APEX的新方法，通过引入无碰撞潜扩散模型同时解决机器人运动规划与操作问题，以应对上述所有困难。首先，我们将现实世界中双臂机器人灵巧操作任务的复杂性进行简化，将其抽象为两个向量的对齐过程。其次，我们设计了潜扩散模型以生成多样化的机器人操作轨迹。进一步，我们利用分类器引导技术整合障碍物信息，从而保证生成轨迹的可行性与安全性。最后，通过在双臂机器人硬件平台上开展的大量实验，我们验证了所提出算法在多样化任务中均能持续生成成功且平滑的轨迹，性能超越传统机器人运动规划算法。这些结果对未来扩散机器人的设计具有重要启示，能够增强其以更高效率和安全性应对更复杂操作任务的能力。实验完整视频演示请参见https://sites.google.com/view/apex-dual-arm/home。