# Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation

arXiv: https://arxiv.org/abs/2506.17198 · HTML: https://mlsanity.com/paper/2506.17198/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 56 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 30 of 41)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI](https://mlsanity.com/paper/2410.00425/) (2024)
- [Deep Dynamics Models for Learning Dexterous Manipulation](https://mlsanity.com/paper/1909.11652/) (2019)
- [DexMV: Imitation Learning for Dexterous Manipulation from Human Videos](https://mlsanity.com/paper/2108.05877/) (2021)
- [ContactPose: A Dataset of Grasps with Object Contact and Hand Pose](https://mlsanity.com/paper/2007.09545/) (2020)
- [Hand-Object Contact Consistency Reasoning for Human Grasps Generation](https://mlsanity.com/paper/2104.03304/) (2021)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [ALOHA Unleashed: A Simple Recipe for Robot Dexterity](https://mlsanity.com/paper/2410.13126/) (2024)
- [CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction](https://mlsanity.com/paper/2012.00924/) (2020)
- [UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy](https://mlsanity.com/paper/2303.00938/) (2023)
- [ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact](https://mlsanity.com/paper/1904.03754/) (2019)
- [Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator](https://mlsanity.com/paper/2104.09194/) (2021)
- [DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects](https://mlsanity.com/paper/2305.05706/) (2023)
- [ContactGen: Generative Contact Modeling for Grasp Generation](https://mlsanity.com/paper/2310.03740/) (2023)
- [Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands](https://mlsanity.com/paper/2208.12250/) (2022)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [DextrAH-RGB: Visuomotor Policies to Grasp Anything with Dexterous Hands](https://mlsanity.com/paper/2412.01791/) (2024)
- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics](https://mlsanity.com/paper/2407.02274/) (2024)
- [Object-Centric Dexterous Manipulation from Human Motion Data](https://mlsanity.com/paper/2411.04005/) (2024)

## Built on by (most cited first, 24 of 24)

- [Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos](https://mlsanity.com/paper/2507.15597/) (2025)
- [Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization](https://mlsanity.com/paper/2601.12993/) (2026)
- [SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation](https://mlsanity.com/paper/2602.16863/) (2026)
- [HumanoidGen: Data Generation for Bimanual Dexterous Manipulation via LLM Reasoning](https://mlsanity.com/paper/2507.00833/) (2025)
- [Spatial-Aware VLA Pretraining through Visual-Physical Alignment from Human Videos](https://mlsanity.com/paper/2512.13080/) (2025)
- [Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration](https://mlsanity.com/paper/2509.09671/) (2025)
- [End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection](https://mlsanity.com/paper/2511.00139/) (2025)
- [Dexora: Open-source VLA for High-DoF Bimanual Dexterity](https://mlsanity.com/paper/2605.18722/) (2026)
- [Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization](https://mlsanity.com/paper/2602.09722/) (2026)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [Human Universal Grasping](https://mlsanity.com/paper/2606.17054/) (2026)
- [From Power to Precision: Learning Fine-grained Dexterity for Multi-fingered Robotic Hands](https://mlsanity.com/paper/2511.13710/) (2025)
- [Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands](https://mlsanity.com/paper/2609.15726/) (2026)
- [UniFucGrasp: Human-Hand-Inspired Unified Functional Grasp Annotation Strategy and Dataset for Diverse Dexterous Hands](https://mlsanity.com/paper/2508.03339/) (2025)
- [DexCanvas: Bridging Human Demonstrations and Robot Learning for Dexterous Manipulation](https://mlsanity.com/paper/2510.15786/) (2025)
- [UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations](https://mlsanity.com/paper/2512.11609/) (2025)
- [SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds](https://mlsanity.com/paper/2604.08544/) (2026)
- [AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection](https://mlsanity.com/paper/2606.23689/) (2026)
- [Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?](https://mlsanity.com/paper/2606.26428/) (2026)
- [Learning to Grasp Anything by Playing with Random Toys](https://mlsanity.com/paper/2510.12866/) (2025)
- [DexHoldem: Playing Texas Hold'em with Dexterous Embodied System](https://mlsanity.com/paper/2605.18727/) (2026)
- [KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation](https://mlsanity.com/paper/2606.09314/) (2026)
- [PLUME: Probabilistic Latent Unified World Modeling and Parameter Estimation for Multi-Finger Manipulation](https://mlsanity.com/paper/2606.11396/) (2026)
- [Mana: Dexterous Manipulation of Articulated Tools](https://mlsanity.com/paper/2606.13677/) (2026)

## Abstract (arXiv)

Generating large-scale demonstrations for dexterous hand manipulation remains challenging, and several approaches have been proposed in recent years to address this. Among them, generative models have emerged as a promising paradigm, enabling the efficient creation of diverse and physically plausible demonstrations. In this paper, we introduce Dex1B, a large-scale, diverse, and high-quality demonstration dataset produced with generative models. The dataset contains one billion demonstrations for two fundamental tasks: grasping and articulation. To construct it, we propose a generative model that integrates geometric constraints to improve feasibility and applies additional conditions to enhance diversity. We validate the model on both established and newly introduced simulation benchmarks, where it significantly outperforms prior state-of-the-art methods. Furthermore, we demonstrate its effectiveness and robustness through real-world robot experiments. Our project page is at https://jianglongye.com/dex1b