# You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations

arXiv: https://arxiv.org/abs/2501.14208 · HTML: https://mlsanity.com/paper/2501.14208/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2025.

## Built on by (most cited first, 21 of 21)

- [Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos](https://mlsanity.com/paper/2507.15597/) (2025)
- [Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations](https://mlsanity.com/paper/2507.00990/) (2025)
- [ConLA: Contrastive Latent Action Learning from Human Videos for Robotic Manipulation](https://mlsanity.com/paper/2602.00557/) (2026)
- [Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization](https://mlsanity.com/paper/2608.26103/) (2026)
- [DexMan: Learning Bimanual Dexterous Manipulation from Human and Generated Videos](https://mlsanity.com/paper/2510.08475/) (2025)
- [From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation](https://mlsanity.com/paper/2605.11951/) (2026)
- [D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation](https://mlsanity.com/paper/2505.04860/) (2025)
- [Uni-Hand: Universal Hand Motion Forecasting in Egocentric Views](https://mlsanity.com/paper/2511.12878/) (2025)
- [SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse](https://mlsanity.com/paper/2603.03836/) (2026)
- [Robot Learning from Human Videos: A Survey](https://mlsanity.com/paper/2604.27621/) (2026)
- [ArtHOI: Taming Foundation Models for Monocular 4D Reconstruction of Hand-Articulated-Object Interactions](https://mlsanity.com/paper/2603.25791/) (2026)
- [Joint Flow Trajectory Optimization For Feasible Robot Motion Generation from Video Demonstrations](https://mlsanity.com/paper/2509.20703/) (2025)
- [EasyMimic: A Low-Cost Framework for Robot Imitation Learning from Human Videos](https://mlsanity.com/paper/2602.11464/) (2026)
- [UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations](https://mlsanity.com/paper/2512.11609/) (2025)
- [CUBic: Coordinated Unified Bimanual Perception and Control Framework](https://mlsanity.com/paper/2605.13452/) (2026)
- [Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer](https://mlsanity.com/paper/2607.28394/) (2026)
- [Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking](https://mlsanity.com/paper/2608.22419/) (2026)
- [One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors](https://mlsanity.com/paper/2510.25241/) (2025)
- [V2P-Manip: Learning Dexterous Manipulation from Monocular Human Videos](https://mlsanity.com/paper/2606.16436/) (2026)
- [C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video](https://mlsanity.com/paper/2608.07045/) (2026)
- [CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements](https://mlsanity.com/paper/2609.07498/) (2026)

## Abstract (arXiv)

Bimanual robotic manipulation is a long-standing challenge of embodied intelligence due to its characteristics of dual-arm spatial-temporal coordination and high-dimensional action spaces. Previous studies rely on pre-defined action taxonomies or direct teleoperation to alleviate or circumvent these issues, often making them lack simplicity, versatility and scalability. Differently, we believe that the most effective and efficient way for teaching bimanual manipulation is learning from human demonstrated videos, where rich features such as spatial-temporal positions, dynamic postures, interaction states and dexterous transitions are available almost for free. In this work, we propose the YOTO (You Only Teach Once), which can extract and then inject patterns of bimanual actions from as few as a single binocular observation of hand movements, and teach dual robot arms various complex tasks. Furthermore, based on keyframes-based motion trajectories, we devise a subtle solution for rapidly generating training demonstrations with diverse variations of manipulated objects and their locations. These data can then be used to learn a customized bimanual diffusion policy (BiDP) across diverse scenes. In experiments, YOTO achieves impressive performance in mimicking 5 intricate long-horizon bimanual tasks, possesses strong generalization under different visual and spatial conditions, and outperforms existing visuomotor imitation learning methods in accuracy and efficiency. Our project link is https://hnuzhy.github.io/projects/YOTO.