UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations

In embodied intelligence, the embodiment gap between roboticand human hands brings significant challenges for learning from humandemonstrations. Although some studies have attempted to bridge thisgap using reinforcement learning, they remain confined to merely re-producing human manipulation, resulting in limited task performance.Moreover, current methods struggle to support diverse robotic hand con-figurations. In this paper, we propose UniBYD, a unified framework thatuses a dynamic reinforcement learning algorithm to discover manipula-tion policies aligned with the robot’s physical characteristics. To enableconsistent modeling across diverse robotic hand morphologies, UniBYDincorporates a unified morphological representation (UMR). Building onUMR, we design a dynamic PPO with an annealed reward schedule, en-abling reinforcement learning to transition from offline-informed imita-tion of human demonstrations to online-adaptive exploration of policiesbetter adapted to diverse robotic morphologies, thereby going beyondmere imitation of human hands. To address the severe state drift causedby the incapacity of early-stage policies, we design a hybrid Markov-based shadow engine that provides fine-grained guidance to anchor theimitation within the expert’s manifold. To evaluate UniBYD, we proposeUniManip, the first benchmark for cross-embodiment manipulation span-ning diverse robotic morphologies. Experiments demonstrate a 44.08%average improvement in success rate over the current state-of-the-art.Our project page is https://zhanheng-creator.github.io/UniBYD/.Mocap Data2-fingered3-fingeredUMRReinforcement learningShadow Engine5-fingeredDynamic PPOUniBYD Our results Imitation-based reinforcement learningFig. 1: Leveraging human demonstrations, UniBYD learns manipulation strategiesthat transcend mere imitation and are tailored to a broad spectrum of robotic handmorphologies.