通过CycleVAE与人类行为Transformer引导示范的跨具身机器人操作合成
Cross-Embodiment Robotic Manipulation Synthesis via Guided Demonstrations through CycleVAE and Human Behavior Transformer
AI总结：
本文提出一种结合CycleVAE与人类行为Transformer的跨具身机器人操作合成方法，通过无监督跨具身对齐和人类示范动力学学习，在灵巧操作任务中生成平滑轨迹并优于先前方法。
摘要：
面向复杂任务的跨具身机器人操作合成具有挑战性，部分原因在于成对的跨具身数据集稀缺，以及设计复杂控制器的阻碍。受通过引导式人类专家示范进行机器人学习的启发，我们在此提出一种新颖的基于CycleVAE和人类行为Transformer的跨具身机器人操作算法。首先，我们利用无监督CycleVAE结合双向子空间对齐算法，在跨具身之间对齐潜在运动序列。其次，我们提出一种因果人类行为Transformer设计，用于学习人类专家示范的内在运动动力学。在测试案例中，我们利用所提出的Transformer生成人类专家示范，随后使用CycleVAE进行对齐，以完成最终的人机操作合成。我们通过使用配备机器人手的灵巧机器人操作器进行的大量实验验证了所提出的算法。我们的结果成功地在复杂任务中生成了平滑轨迹，优于先前基于学习的机器人运动规划算法。这些结果对执行无监督跨具身对齐以及未来自主机器人设计具有重要意义。我们实验的完整视频演示可在 https://sites.google.com/view/humanrobots/home 找到。
英文摘要：
Cross-embodiment robotic manipulation synthesis for complicated tasks is challenging, partially due to the scarcity of paired cross-embodiment datasets and the impediment of designing intricate controllers. Inspired by robotic learning via guided human expert demonstration, we here propose a novel cross-embodiment robotic manipulation algorithm via CycleVAE and human behavior transformer. First, we utilize unsupervised CycleVAE together with a bidirectional subspace alignment algorithm to align latent motion sequences between cross-embodiments. Second, we propose a casual human behavior transformer design to learn the intrinsic motion dynamics of human expert demonstrations. During the test case, we leverage the proposed transformer for the human expert demonstration generation, which will be aligned using CycleVAE for the final human-robotic manipulation synthesis. We validated our proposed algorithm through extensive experiments using a dexterous robotic manipulator with the robotic hand. Our results successfully generate smooth trajectories across intricate tasks, outperforming prior learning-based robotic motion planning algorithms. These results have implications for performing unsupervised cross-embodiment alignment and future autonomous robotics design. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/humanrobots/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。
