Bimanual robotic manipulation remains a fundamental challenge due to the inherent complexity of dual-arm coordination and high-dimensional action spaces. This paper presents the extended YOTO++ (You Only Teach Once), which is a unified one-shot learning framework for teaching bimanual skills directly from third-person human video demonstrations. Our method extracts structured 3D hand motions using binocular vision and distills them into compact, keyframe-based trajectories for dual-arm execution. We develop a scalable demonstration proliferation strategy that synthetically augments one-shot demonstrations into diverse training samples, enabling effective learning of a customized bimanual diffusion policy. Extensive evaluations across a broad spectrum of long-horizon bimanual tasks, including asynchronous, synchronous, contact-rich, and non-prehensile scenarios, demonstrate strong generalization to novel skills and objects. We further introduce a visual alignment mechanism at the initial manipulation stage for closed-loop control, enabling the system to timely adapt to perturbations during execution. We validate the framework on an unseen dual-arm robotic platform to show seamless cross-embodiment transfer without additional retraining. YOTO++ achieves impressive performance in accuracy, robustness, and scalability, advancing the practical deployment of general-purpose bimanual manipulation systems.

A critical prerequisite for achieving generalizable robot control is the availability of a large-scale robot training dataset. Due to the expense of collecting realistic robotic data, recent studies explored simulating and recording robot skills in virtual environments. While simulated data can be generated at higher speeds, lower costs, and larger scales, the applicability of such simulated data remains questionable due to the gap between simulated and realistic environments. To advance the Sim2Real generalization, in this study, we present DexScale, a data engine designed to perform automatic skills simulation and scaling for learning deployable robot manipulation policies. Specifically, DexScale ensures the usability of simulated skills by integrating diverse forms of realistic data into the simulated environment, preserving semantic alignment with the target tasks. For each simulated skill in the environment, DexScale facilitates effective Sim2Real data scaling by automating the process of domain randomization and adaptation. Tuned by the scaled dataset, the control policy achieves zero-shot Sim2Real generalization across diverse tasks, multiple robot embodiments, and widely studied policy model architectures, highlighting its importance in advancing Sim2Real embodied intelligence.

Learning robotic grasps from visual observations is a promising yet challenging task. Recent research shows its great potential by preparing and learning from large-scale synthetic datasets. For the popular, 6 degree-of-freedom (6-DOF) grasp setting of parallel-jaw gripper, most of existing methods take the strategy of heuristically sampling grasp candidates and then evaluating them using learned scoring functions. This strategy is limited in terms of the conflict between sampling efficiency and coverage of optimal grasps. To this end, we propose in this work a novel, end-to-end \emph{Grasp Proposal Network (GPNet)}, to predict a diverse set of 6-DOF grasps for an unseen object observed from a single and unknown camera view. GPNet builds on a key design of grasp proposal module that defines \emph{anchors of grasp centers} at discrete but regular 3D grid corners, which is flexible to support either more precise or more diverse grasp predictions. To test GPNet, we contribute a synthetic dataset of 6-DOF object grasps; evaluation is conducted using rule-based criteria, simulation test, and real test. Comparative results show the advantage of our methods over existing ones. Notably, GPNet gains better simulation results via the specified coverage, which helps achieve a ready translation in real test. Our code and dataset are available on \url{https://github.com/CZ-Wu/GPNet}.