1 citations · 1 across the 12 of their papers we have counts for
12 papers
An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond
Wenbo Zhang, Kaixuan Wang, Yutao Ouyang +9
Embodied AI systems are often organized into System 1 and System 2. System 1 is typically a pretrained policy that generates actions at high frequency, whereas System 2 is often in…
XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment
XPolicyLab Community, Tianxing Chen, Yue Chen +67
Robot policy evaluation and deployment remain fragmented by model-specific software dependencies, data representations, and runtime interfaces, so that connecting N policies to M e…
Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations
Jialiang Li, Yuhan Wang, Haojun Li +5
General-purpose robotic manipulation requires robots to perform diverse tasks in open-world environments while improving their skills over time. Despite recent progress in robotic…
RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination
Haotian Liang, Mingkang Chen, Yufei Huang +27
Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation…
dVLA-RL: Reinforcement Learning over Denoising Trajectories for Discrete Diffusion Vision-Language-Action Models
Yuhao Wu, Yitian Liu, Weijie Shen +13
Vision-Language-Action (VLA) models have established a powerful paradigm for generalist robotic manipulation by grounding control into the semantic reasoning of VLMs. Prevailing ar…
HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System
Tianshuo Yang, Guanyu Chen, Yutian Chen +8
While end-to-end Vision-Language-Action (VLA) models offer a promising paradigm for robotic manipulation, fine-tuning them on narrow control data often compromises the profound rea…
ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents
Fanqing Meng, Lingxiao Du, Zijian Wu +46
Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change in…
MM-ACT: Learn from Multimodal Parallel Generation to Act
Haotian Liang, Xinyi Chen, Bin Wang +12
A generalist robotic policy needs both semantic understanding for task planning and the ability to interact with the environment through predictive capabilities. To tackle this, we…
xGR: Efficient Generative Recommendation Serving at Scale
Qingxiao Sun, Tongxuan Liu, Shen Zhang +13
Recommendation system delivers substantial economic benefits by providing personalized predictions. Generative recommendation (GR) integrates LLMs to enhance the understanding of l…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
Junting Chen, Haotian Liang, Lingxiao Du +8
The rapid progress of navigation, manipulation, and vision models has made mobile manipulators capable in many specialized tasks. However, the open-world mobile manipulation (OWMM)…
We Choose to Go to Space: Agent-driven Human and Multi-Robot Collaboration in Microgravity
Miao Xin, Zhongrui You, Zihan Zhang +7
We present SpaceAgents-1, a system for learning human and multi-robot collaboration (HMRC) strategies under microgravity conditions. Future space exploration requires humans to wor…