H-RDT：人类操作增强的双臂机器人操作
H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation
AI总结：
针对机器人操作模仿学习的高质量演示数据稀缺问题，本文提出H-RDT框架，通过两阶段训练利用人类操作数据增强双臂机器人操作能力，在仿真和真实实验中均优于现有方法。
摘要：
机器人操作的模仿学习面临一项根本性挑战：大规模、高质量的机器人演示数据稀缺。近期的机器人基础模型通常在跨具身机器人数据集上进行预训练以扩大数据规模，但由于不同机器人具身的形态和动作空间存在差异，统一训练面临显著局限。本文提出H-RDT（Human to Robotics Diffusion Transformer，人类到机器人扩散Transformer），这是一种利用人类操作数据增强机器人操作能力的新方法。我们的核心洞见是：带有配对3D手部姿态标注的大规模第一人称人类操作视频提供了丰富的行为先验，这些先验捕捉了自然的操作策略，可助力机器人策略学习。我们提出了两阶段训练范式：（1）在大规模第一人称人类操作数据上预训练；（2）通过模块化动作编码器和解码器，在机器人特定数据上进行跨具身微调。H-RDT基于2B参数的扩散Transformer架构构建，采用流匹配建模复杂动作分布。涵盖仿真与真实世界实验、单任务与多任务场景以及少样本学习和鲁棒性评估的大量实验表明，H-RDT性能优于从头训练和现有最优方法（包括Pi0和RDT），在仿真和真实世界实验中分别较从头训练取得13.9%和40.5%的显著提升。该结果验证了我们的核心假设：人类操作数据可作为学习双臂机器人操作策略的有力基础。
英文摘要：
Imitation learning for robotic manipulation faces a fundamental challenge: the scarcity of large-scale, high-quality robot demonstration data. Recent robotic foundation models often pre-train on cross-embodiment robot datasets to increase data scale, while they face significant limitations as the diverse morphologies and action spaces across different robot embodiments make unified training challenging. In this paper, we present H-RDT (Human to Robotics Diffusion Transformer), a novel approach that leverages human manipulation data to enhance robot manipulation capabilities. Our key insight is that large-scale egocentric human manipulation videos with paired 3D hand pose annotations provide rich behavioral priors that capture natural manipulation strategies and can benefit robotic policy learning. We introduce a two-stage training paradigm: (1) pre-training on large-scale egocentric human manipulation data, and (2) cross-embodiment fine-tuning on robot-specific data with modular action encoders and decoders. Built on a diffusion transformer architecture with 2B parameters, H-RDT uses flow matching to model complex action distributions. Extensive evaluations encompassing both simulation and real-world experiments, single-task and multitask scenarios, as well as few-shot learning and robustness assessments, demonstrate that H-RDT outperforms training from scratch and existing state-of-the-art methods, including Pi0 and RDT, achieving significant improvements of 13.9% and 40.5% over training from scratch in simulation and real-world experiments, respectively. The results validate our core hypothesis that human manipulation data can serve as a powerful foundation for learning bimanual robotic manipulation policies.
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。
