2 citations · 4 across the 21 of their papers we have counts for
30 papers
Uni-PNP: Unified Prehensile and Non-prehensile Policy Learning for Generalizable Object Pose Reaching
Wenxuan Zhu, Yuze Wang, Jiangran Lyu +6
Object manipulation often requires combining non-prehensile actions with grasping. However, existing approaches typically develop these capabilities separately and integrate them b…
Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies
Galbot Team, Xuchuan Chen, Xiaoqian Cheng +31
GPT-6 Astra exhibits a remarkable ability to generate numerical robot actions, extending its role beyond high-level planning. To assess Astra's capabilities as general-purpose embo…
DualWAM: Dual-System World Action Models for Asynchronous Global Planning and Local Refinement
Yixin Zheng, Jiangran Lyu, Yuntian Deng +6
World Action Models (WAMs) jointly generate robot actions and predict future world states, transferring priors from video pretraining to robot control. However, future visual predi…
GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning
Long Xu, Zhiqi Zhang, Mi Yan +8
Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment for both. Automated scene ge…
ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation
Mi Yan, Wenhao Zhang, Zhiqi Zhang +14
Zero-shot generalization to unseen embodiments is important for generalizable vision-language-action (VLA) models as robot hardware evolves and task-specific data collection remain…
WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time
Yusen Feng, Bingchen Han, Jiangran Lyu +13
Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot demonstrations, task-specific fin…
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
AllDayNav: Lifelong Navigation via Real-World Reinforcement Learning
Hang Yin, Yinan Liang, Jiazhao Zhang +4
Lifelong embodied navigation in dynamic environments requires robots to form persistent scene understanding from fragmentary observations, which remains difficult for existing meth…
MobiGuide: Vocabulary-Guided Online Recovery for Mobile Manipulation
Jia Syuen Lim, Zhizhen Zhang, Peter Bohm +3
A central challenge in mobile manipulation is preserving multiple plausible action modes while remaining reactive during execution. For example, grasping a bottle in a cluttered sc…
NavGSim: High-Fidelity Gaussian Splatting Simulator for Large-Scale Navigation
Jiahang Liu, Yuanxing Duan, Jiazhao Zhang +4
Simulating realistic environments for robots is widely recognized as a critical challenge in robot learning, particularly in terms of rendering and physical simulation. This challe…
SPAN-Nav: Generalized Spatial Awareness for Versatile Embodied Navigation
Jiahang Liu, Tianyu Xu, Jiawei Chen +9
Recent embodied navigation approaches leveraging Vision-Language Models (VLMs) demonstrate strong generalization in versatile Vision-Language Navigation (VLN). However, reliable pa…
SimRecon: SimReady Compositional Scene Reconstruction from Real Videos
Chong Xia, Kai Zhu, Zizhuo Wang +3
Compositional scene reconstruction seeks to create object-centric representations rather than holistic scenes from real-world videos, which is natively applicable for simulation an…
Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning
Yixin Zheng, Jiangran Lyu, Yifan Zhang +8
Extrinsic dexterity leverages environmental contact to overcome the limitations of prehensile manipulation. However, achieving such dexterity in cluttered scenes remains challengin…
LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion
Jiangran Lyu, Kai Liu, Xuheng Zhang +20
Recent robot foundation models largely rely on large-scale behavior cloning, which imitates expert actions but discards transferable dynamics knowledge embedded in heterogeneous em…
Any3D-VLA: Enhancing VLA Robustness via Diverse Point Clouds
Xianzhe Fan, Shengliang Deng, Xiaoyang Wu +7
Existing Vision-Language-Action (VLA) models typically take 2D images as visual input, which limits their spatial understanding in complex scenes. How can we incorporate 3D informa…
StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision
Shengliang Deng, Mi Yan, Yixin Zheng +7
While Vision-Language-Action (VLA) models excel in generalist manipulation, they often lack fine-grained spatial awareness and show limited viewpoint robustness. This limitation la…
UrbanVLA: A Vision-Language-Action Model for Urban Micromobility
Anqi Li, Zhiyong Wang, Jiazhao Zhang +5
Urban micromobility applications, such as delivery robots, demand reliable navigation across large-scale urban environments while following long-horizon route instructions. This ta…
TrackVLA++: Unleashing Reasoning and Memory Capabilities in VLA Models for Embodied Visual Tracking
Jiahang Liu, Yunpeng Qi, Jiazhao Zhang +9
Embodied Visual Tracking (EVT) is a fundamental ability that underpins practical applications, such as companion robots, guidance robots and service assistants, where continuously…
MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning
Tianyu Xu, Jiawei Chen, Jiazhao Zhang +5
Visual navigation policy is widely regarded as a promising direction, as it mimics humans by using egocentric visual observations for navigation. However, optical information of vi…
Embodied Navigation Foundation Model
Jiazhao Zhang, Anqi Li, Yunpeng Qi +14
Navigation is a fundamental capability in embodied AI, representing the intelligence required to perceive and interact within physical environments following language instructions.…
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
Wenyao Zhang, Hongsi Liu, Zekun Qi +11
Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot ma…
DexVLG: Dexterous Vision-Language-Grasp Model at Scale
Jiawei He, Danshi Li, Xinqiang Yu +7
As large models gain traction, vision-language-action (VLA) systems are enabling robots to tackle increasingly complex tasks. However, limited by the difficulty of data collection,…
CLAR: Learning 3D Representations for Robotic Manipulation by Fusing Masked Reconstruction with Multi-Level Contrastive Alignment
Wenbo Cui, Chengyang Zhao, Yuhui Chen +4
The spatial information inherent in 3D point clouds is crucial for robotic manipulation. However, existing 3D pre-training methods face a fundamental trade-off: Masked Autoencoding…
TrackVLA: Embodied Visual Tracking in the Wild
Shaoan Wang, Jiazhao Zhang, Minghan Li +7
Embodied visual tracking is a fundamental skill in Embodied AI, enabling an agent to follow a specific target in dynamic environments using only egocentric vision. This task is inh…