APEX：使用无碰撞生成扩散模型的灵巧双臂机器人操作
APEX: Ambidextrous Dual-Arm Robotic Manipulation Using Collision-Free Generative Diffusion Models
AI总结：
本文提出 APEX，将双臂操作抽象为双向量对齐，并用分类器引导的潜在扩散模型生成无碰撞、可行且安全的双臂轨迹，硬件实验证明其优于传统运动规划方法。
摘要：
灵巧操作，尤其是熟练的协调与抓取，是机器人的一项基本且不可或缺的能力，有助于模仿人类行为。将这种能力集成到机器人中，使其能够在日常生活和工业环境中辅助甚至替代人类承担日益复杂的任务。遗憾的是，由于任务复杂、机器人操作空间广阔以及存在动态障碍物，当代方法在设计操作轨迹时面临严峻挑战。我们提出一种名为 APEX 的新方法，通过引入同时用于机器人运动规划和操作的无碰撞潜在扩散模型来解决所有这些困难。首先，我们将现实中的灵巧双臂机器人操作任务抽象为对齐两个向量，从而简化其复杂性。其次，我们设计潜在扩散模型以生成多样化的机器人操作轨迹。此外，我们利用分类器引导技术整合障碍物信息，从而保证所生成操作轨迹的可行性和安全性。最后，我们在灵巧双臂机器人硬件平台上开展大量实验，验证所提出的算法。我们的算法在多种任务中持续生成成功且流畅无缝的轨迹，优于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意义，可提升其以更高效率和安全性处理更复杂机器人操作任务的能力。完整实验视频演示可见 https://sites.google.com/view/apex-dual-arm/home。
英文摘要：
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.
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。
