GR-3 Technical Report
- URL: http://arxiv.org/abs/2507.15493v2
- Date: Tue, 22 Jul 2025 15:04:37 GMT
- Title: GR-3 Technical Report
- Authors: Chilam Cheang, Sijin Chen, Zhongren Cui, Yingdong Hu, Liqun Huang, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Xiao Ma, Hao Niu, Wenxuan Ou, Wanli Peng, Zeyu Ren, Haixin Shi, Jiawen Tian, Hongtao Wu, Xin Xiao, Yuyang Xiao, Jiafeng Xu, Yichu Yang,
- Abstract summary: GR-3 is a large-scale vision-language-action (VLA) model.<n>It showcases exceptional capabilities in generalizing to novel objects, environments, and instructions involving abstract concepts.<n> GR-3 excels in handling long-horizon and dexterous tasks, including those requiring bi-manual manipulation and mobile movement.
- Score: 21.857666871078933
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We report our recent progress towards building generalist robot policies, the development of GR-3. GR-3 is a large-scale vision-language-action (VLA) model. It showcases exceptional capabilities in generalizing to novel objects, environments, and instructions involving abstract concepts. Furthermore, it can be efficiently fine-tuned with minimal human trajectory data, enabling rapid and cost-effective adaptation to new settings. GR-3 also excels in handling long-horizon and dexterous tasks, including those requiring bi-manual manipulation and mobile movement, showcasing robust and reliable performance. These capabilities are achieved through a multi-faceted training recipe that includes co-training with web-scale vision-language data, efficient fine-tuning from human trajectory data collected via VR devices, and effective imitation learning with robot trajectory data. In addition, we introduce ByteMini, a versatile bi-manual mobile robot designed with exceptional flexibility and reliability, capable of accomplishing a wide range of tasks when integrated with GR-3. Through extensive real-world experiments, we show GR-3 surpasses the state-of-the-art baseline method, $\pi_0$, on a wide variety of challenging tasks. We hope GR-3 can serve as a step towards building generalist robots capable of assisting humans in daily life.
Related papers
- Is Diversity All You Need for Scalable Robotic Manipulation? [50.747150672933316]
We investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional intuition of "more diverse is better"<n>We show that task diversity proves more critical than per-task demonstration quantity, benefiting transfer from diverse pre-training tasks to novel downstream scenarios.<n>We propose a distribution debiasing method to mitigate velocity ambiguity, the yielding GO-1-Pro achieves substantial performance gains of 15%, equivalent to using 2.5 times pre-training data.
arXiv Detail & Related papers (2025-07-08T17:52:44Z) - Tool-as-Interface: Learning Robot Policies from Human Tool Usage through Imitation Learning [16.394434999046293]
We propose a framework to transfer tool-use knowledge from humans to robots.<n>We validate our approach on diverse real-world tasks, including meatball scooping, pan flipping, wine bottle balancing, and other complex tasks.
arXiv Detail & Related papers (2025-04-06T20:40:19Z) - REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation [57.628771707989166]
We propose an adaptive multi-agent planning framework, termed REMAC, that enables efficient, scene-agnostic multi-robot long-horizon task planning and execution.<n>ReMAC incorporates two key modules: a self-reflection module performing pre-conditions and post-condition checks in the loop to evaluate progress and refine plans, and a self-evolvement module dynamically adapting plans based on scene-specific reasoning.
arXiv Detail & Related papers (2025-03-28T03:51:40Z) - AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems [88.05152114775498]
AgiBot World is a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios.<n>AgiBot World guarantees high-quality and diverse data distribution.<n>GO-1 exhibits exceptional capability in real-world dexterous and long-horizon tasks.
arXiv Detail & Related papers (2025-03-09T15:40:29Z) - BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities [45.61898182253723]
We introduce the BEHAVIOR Robot Suite (BRS), a comprehensive framework for whole-body manipulation in diverse household tasks.<n>Built on a bimanual, wheeled robot with a 4-DoF torso, BRS integrates a cost-effective whole-body teleoperation interface for data collection and a novel algorithm for learning whole-body visuomotor policies.
arXiv Detail & Related papers (2025-03-07T18:15:21Z) - Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models [5.2364456910271935]
We propose an unsupervised pipeline to generate reward functions from natural language task descriptions.<n>The rewards are used to train RL agents in simulated environments, where we formalize the reward generation process to enhance feasibility.<n>Our approach is validated through extensive simulated experiments on single-arm and bi-manual manipulation tasks using an ABB YuMi collaborative robot.
arXiv Detail & Related papers (2025-03-06T10:08:44Z) - TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies [95.30717188630432]
We introduce visual trace prompting to facilitate VLA models' spatial-temporal awareness for action prediction.<n>We develop a new TraceVLA model by finetuning OpenVLA on our own collected dataset of 150K robot manipulation trajectories.<n>We present a compact VLA model based on 4B Phi-3-Vision, pretrained on the Open-X-Embodiment and finetuned on our dataset.
arXiv Detail & Related papers (2024-12-13T18:40:51Z) - WildLMa: Long Horizon Loco-Manipulation in the Wild [18.542469512253295]
'In-the-wild' mobile manipulation aims to deploy robots in diverse real-world environments.<n>This paper proposes WildLMa with three components to address these issues.
arXiv Detail & Related papers (2024-11-22T18:56:56Z) - Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments [26.66666135624716]
We present Robot Utility Models (RUMs), a framework for training and deploying zero-shot robot policies.
RUMs can generalize to new environments without any finetuning.
We train five utility models for opening cabinet doors, opening drawers, picking up napkins, picking up paper bags, and reorienting fallen objects.
arXiv Detail & Related papers (2024-09-09T17:59:50Z) - RT-1: Robotics Transformer for Real-World Control at Scale [98.09428483862165]
We present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties.
We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks.
arXiv Detail & Related papers (2022-12-13T18:55:15Z) - GNM: A General Navigation Model to Drive Any Robot [67.40225397212717]
General goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots.
We analyze the necessary design decisions for effective data sharing across robots.
We deploy the trained GNM on a range of new robots, including an under quadrotor.
arXiv Detail & Related papers (2022-10-07T07:26:41Z) - Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human
Videos [59.58105314783289]
Domain-agnostic Video Discriminator (DVD) learns multitask reward functions by training a discriminator to classify whether two videos are performing the same task.
DVD can generalize by virtue of learning from a small amount of robot data with a broad dataset of human videos.
DVD can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.
arXiv Detail & Related papers (2021-03-31T05:25:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.