8 citations · 21 across the 36 of their papers we have counts for
38 papers
GigaBrain-WBC-0.5: A Behavior World Model for Robust Humanoid Whole-Body Tracking with Environment Interaction
Ziyang Cheng, Tianshu Tang, Jinxin Lan +19
General-purpose motion trackers enable humanoid robots to follow diverse whole-body motions while maintaining balance, but are trained only on flat ground, failing to exploit biped…
GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture
GigaBrain Team, Angen Ye, Axiang Sun +56
Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured sett…
Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment
Boyuan Wang, Zhenyuan Zhang, Zhiqin Yang +16
Autonomous data collection governs the volume and quality of real-world trajectories for manipulation policy learning. Existing pipelines reduce human effort via self-resetting, VL…
GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
GigaWorld Team, Angen Ye, Angyuan Ma +26
World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as dense supervision for physicall…
GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation
GigaWorld Team, Angyuan Ma, Boyuan Wang +24
Evaluating embodied robot foundation models remains a critical bottleneck; unlike large language models efficiently assessed via digital benchmarks, robotic policies require slow,…
StableIDM: Stabilizing Inverse Dynamics Model against Manipulator Truncation via Spatio-Temporal Refinement
Kerui Li, Zhe Jing, Xiaofeng Wang +6
Inverse Dynamics Models (IDMs) map visual observations to low-level action commands, serving as central components for data labeling and policy execution in embodied AI. However, t…
VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis
Xiaolei Lang, Yang Wang, Yukun Zhou +10
Recent advances in robot foundation models trained on large-scale human teleoperation data have enabled robots to perform increasingly complex real-world tasks. However, scaling th…
ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video
Boyuan Wang, Xiaofeng Wang, Yongkang Li +9
Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for per-scene optimization, recov…
DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning
Yang Zhou, Xiaofeng Wang, Hao Shao +8
Recently, world-action models (WAM) have emerged to bridge vision-language-action (VLA) models and world models, unifying their reasoning and instruction-following capabilities and…
ViVa: A Video-Generative Value Model for Robot Reinforcement Learning
Jindi Lv, Hao Li, Jie Li +11
Vision-language-action (VLA) models have advanced robot manipulation through large-scale pretraining, but real-world deployment remains challenging due to partial observability and…
FlashSign: Pose-Free Guidance for Efficient Sign Language Video Generation
Liuzhou Zhang, Zeyu Zhang, Biao Wu +10
Sign language plays a crucial role in bridging communication gaps between the deaf and hard-of-hearing communities. However, existing sign language video generation models often re…
GigaWorld-Policy: An Efficient Action-Centered World--Action Model
Angen Ye, Boyuan Wang, Chaojun Ni +21
World-Action Models (WAM) initialized from pre-trained video generation backbones have demonstrated remarkable potential for robot policy learning. However, existing approaches fac…
GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning
GigaBrain Team, Boyuan Wang, Bohan Li +23
Vision-language-action (VLA) models that directly predict multi-step action chunks from current observations face inherent limitations due to constrained scene understanding and we…
UniDriveDreamer: A Single-Stage Multimodal World Model for Autonomous Driving
Guosheng Zhao, Yaozeng Wang, Xiaofeng Wang +10
World models have demonstrated significant promise for data synthesis in autonomous driving. However, existing methods predominantly concentrate on single-modality generation, typi…
Multi-Path Collaborative Reasoning via Reinforcement Learning
Jindi Lv, Yuhao Zhou, Zheng Zhu +3
Chain-of-Thought (CoT) reasoning has significantly advanced the problem-solving capabilities of Large Language Models (LLMs), yet conventional CoT often exhibits internal determini…
SwiftVLA: Unlocking Spatiotemporal Dynamics for Lightweight VLA Models at Minimal Overhead
Chaojun Ni, Cheng Chen, Xiaofeng Wang +12
Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts. To…
GigaWorld-0: World Models as Data Engine to Empower Embodied AI
GigaWorld Team, Angen Ye, Boyuan Wang +22
World models are emerging as a foundational paradigm for scalable, data-efficient embodied AI. In this work, we present GigaWorld-0, a unified world model framework designed explic…
GigaBrain-0: A World Model-Powered Vision-Language-Action Model
GigaBrain Team, Angen Ye, Boyuan Wang +24
Training Vision-Language-Action (VLA) models for generalist robots typically requires large-scale real-world robot data, which is expensive and time-consuming to collect. The ineff…
DriveGen3D: Boosting Feed-Forward Driving Scene Generation with Efficient Video Diffusion
Weijie Wang, Jiagang Zhu, Zeyu Zhang +14
We present DriveGen3D, a novel framework for generating high-quality and highly controllable dynamic 3D driving scenes that addresses critical limitations in existing methodologies…
MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training
Haoyun Li, Ivan Zhang, Runqi Ouyang +12
Vision Language Action (VLA) models derive their generalization capability from diverse training data, yet collecting embodied robot interaction data remains prohibitively expensiv…
EgoDemoGen: Egocentric Demonstration Generation for Viewpoint Generalization in Robotic Manipulation
Yuan Xu, Jiabing Yang, Xiaofeng Wang +16
Imitation learning based visuomotor policies have achieved strong performance in robotic manipulation, yet they often remain sensitive to egocentric viewpoint shifts. Unlike third-…
EMMA: Enhancing Robotic Control Robustness through a Generative Visual Model
Zhehao Dong, Xiaofeng Wang, Zheng Zhu +12
The generalization of vision-language-action (VLA) models heavily relies on diverse training data. However, acquiring large-scale robot manipulation data across varied object appea…
ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction
Chaojun Ni, Guosheng Zhao, Xiaofeng Wang +6
Reinforcement learning for training end-to-end autonomous driving models in closed-loop simulations is gaining growing attention. However, most simulation environments differ signi…
Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
Wei Cui, Haoyu Wang, Wenkang Qin +19
Humanoid robot technology is advancing rapidly, with manufacturers introducing diverse heterogeneous visual perception modules tailored to specific scenarios. Among various percept…