Cross-Embodiment Robotic Manipulation Synthesis via Guided Demonstrations through CycleVAE and Human Behavior Transformer

Abstract

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.