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. The modular design of action encoder and decoder components enables effective knowledge transfer from the unified human embodiment to diverse robot platforms through efficient fine-tuning. 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 π0 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.

Fine-tuning is a typical mechanism to achieve model adaptation for mobile/web users, where a model trained by the cloud is further retrained to fit the target user task. While traditional fine-tuning has been proved effective, it only utilizes local data to achieve adaptation, failing to take advantage of the valuable knowledge from other mobile/web users. In this paper, we attempt to extend the local-user fine-tuning to multi-user fed-tuning with the help of Federated Learning (FL). Following the new paradigm, we propose EEFT, a framework aiming to achieve Efficient and Effective Fed-Tuning for mobile/web users. The key idea is to introduce lightweight but effective adaptation modules to the pre-trained model, such that we can freeze the pre-trained model and just focus on optimizing the modules to achieve cost reduction and selective task cooperation. Extensive experiments on our constructed benchmark demonstrate the effectiveness and efficiency of the proposed framework.