NVIDIA Seattle Robotics Lab

At the NVIDIA Seattle Robotics Lab, we strive to fulfill NVIDIA’s robotics mission: developing the essential technology that can enable any company to become a robotics company. We conduct fundamental and applied robotics research across the full robotics stack, including perception, planning, control, reinforcement learning, imitation learning, simulation, and vision-language-action models. Through our work, we aim to transform research paradigms, transfer technology into NVIDIA’s robotics and simulation products, and create new robotics markets for the world.

🤖 Join Us

Are you passionate about robotics research and eager to make an impact? The NVIDIA Seattle Robotics Lab is hiring for full-time research positions! Join our world-class team to tackle grand challenges in robotics, conducting fundamental and applied research across perception, planning, control, reinforcement learning, and more.

🚀 Open Positions

Research Areas

Perception

Perception is essential for robots to understand the world. Our lab has advanced vision-based policy learning through techniques such as Perceiver-Actor, RVT, and RVT-2, as well as multimodal sensor simulation through simulation and learning frameworks such as TacSL. A major challenge ahead is scaling visuomotor policies to open-world environments with diverse objects and dynamic scenes, and integrating multimodal sensing (vision, touch, force-torque) to achieve robust, dexterous manipulation.

Task and Motion Planning

Task and motion planning (TAMP) enables robots to coordinate and sequence high-level, long-horizon goals with low-level, short-horizon skills. We have developed GPU-accelerated planners such as cuTAMP and cuRobo to rapidly solve complex, long-horizon tasks and generate collision-free motion plans. Key challenges ahead include achieving real-time TAMP in dynamic, partially observable environments and extending planning algorithms to reason about contact-rich, deformable, and uncertain settings.

Control

Control allows robots to satisfy pose, velocity, and force targets with high precision, robustness, and safety. Our team is developing high-performance control libraries for manipulators and humanoids, and we have explored applying these algorithms to settings involving real-world reinforcement learning and high-precision assembly. An open challenge is developing controllers that unify model-based precision and learning-based adaptability, enabling robust performance across varying tasks and environments, especially in contact-rich and safety-critical applications.

Reinforcement Learning

Reinforcement learning (RL) empowers robots to learn complex skills through trial and error, reducing human effort and enabling superhuman performance. We explore simulation-driven model-based and model-free RL for robotics through works such as SHAC and IndustReal, as well as principled domain randomization approaches such as BayesSim. A major challenge ahead is bridging the sim-to-real gap by combining scalable simulation-driven RL with efficient real-world adaptation, and incorporating priors and constraints to ensure safety and generalization.

Imitation Learning

Imitation learning enables robots to rapidly acquire new skills quickly by learning from human demonstrations. Our research has focused on automated data generation through frameworks such as MimicGen, SkillMimicGen, and DexMimicGen, as well as developing hierarchical vision-language-action (VLA) models such as HAMSTER that can learn from off-domain data. Opportunities ahead include generating diverse, high-fidelity datasets for contact-rich and long-horizon tasks, and advancing beyond current behavior cloning paradigms (e.g., diffusion models) to methods capable of structured, hierarchical skill acquisition.

Simulation

Simulation enables large-scale training, testing, and benchmarking in virtual environments. We have developed GPU-accelerated simulation frameworks like Factory, differentiable simulators like DiSECT, and sim-to-real frameworks like AutoMate and FORGE for contact-rich tasks. Grand challenges ahead include improving physical realism (e.g., contact interactions, deformables, fluids) while maintaining GPU-accelerated performance, and closing the sim-to-real loop through online real-world feedback and continual refinement.

Human-Robot Interaction

Human-Robot Interaction (HRI) is critical for building robots that safely and intuitively collaborate with people in shared environments. Our research has included model-predictive control for fluent human-robot handovers, as well as ITPS for real-time policy steering. Major opportunities ahead involve leveraging foundation models and multimodal learning for intuitive, context-aware interaction, and enabling fluid, anticipatory collaboration with humans in industrial and household settings.

Intern Alumni

Discover our talented former interns and their contributions to robotics research.

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Publications

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Sreevardhan Sirigiri, Christian Hughes, Ian Abraham, Fabio Ramos

December, 2025 NeurIPS 2025

Diversifying Parallel Ergodic Search: A Signature Kernel Evolution Strategy

Shuo Cheng, Liqian Ma, Zhenyang Chen, Ajay Mandlekar, Caelan Reed Garrett, Danfei Xu

December, 2025 NeurIPS 2025

Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training

Chunru Lin, Haotian Yuan, Yian Wang, Xiaowen Qiu, Tsun-Hsuan Wang, Minghao Guo, Bohan Wang, Yashraj Narang, Dieter Fox, Chuang Gan

December, 2025 NeurIPS 2025

RobotSmith: Generative Robotic Tool Design for Acquisition of Complex Manipulation Skills

Herbert Wright, Weiming Zhi, Martin Matak, Matthew Johnson-Roberson, Tucker Hermans

November, 2025 RA-L

Robust Bayesian Scene Reconstruction by Leveraging Retrieval-Augmented Priors

Krishnan Srinivasan, Jie Xu, Henry Ang, Eric Heiden, Dieter Fox, Jeannette Bohg, Animesh Garg

October, 2025 Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ACGD: Visual Multitask Policy Learning with Asymmetric Critic Guided Distillation

Sirui Xu, Yu-Wei Chao, Liuyu Bian, Arsalan Mousavian, Yu-Xiong Wang, Liangyan Gui, Wei Yang

September, 2025 Proceedings of the Conference on Robot Learning (CoRL)

Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration

Yixuan Huang, Novella Alvina, Mohanraj Devendran Shanthi, Tucker Hermans

September, 2025 Proceedings of the Conference on Robot Learning (CoRL)

Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference

Jie Xu, Eric Heiden, Iretiayo Akinola, Dieter Fox, Miles Macklin, Yashraj Narang

September, 2025 Proceedings of the Conference on Robot Learning (CoRL)

Neural Robot Dynamics

Binghao Huang, Jie Xu, Iretiayo Akinola, Wei Yang, Balakumar Sundaralingam, Rowland O'Flaherty, Dieter Fox, Xiaolong Wang, Arsalan Mousavian, Yu-Wei Chao, Yunzhu Li*

September, 2025 Proceedings of the Conference on Robot Learning (CoRL)

VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning

Shengyi Qian, Kaichun Mo, Valts Blukis, David Fouhey, Dieter Fox, Ankit Goyal

June, 2025 Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR)

3D-MVP: 3D Multiview Pretraining for Robotic Manipulation

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Contact

ynarang@nvidia.com

NVIDIA Seattle Robotics Lab

🤖 Join Us

🚀 Open Positions

Research Areas

Perception

Task and Motion Planning

Control

Reinforcement Learning

Imitation Learning

Simulation

Human-Robot Interaction

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