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.

View Intern Alumni

Publications

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Rishit Dagli, Donglai Xiang, Vismay Modi, Xuning Yang, Gavriel State, David Levin, Maria Shugrina

July, 2026 Proceedings of the International Conference on Machine Learning (ICML)

Adaptive Volumetric Mechanical Property Fields Invariant to Resolution

Christian Hughes, Yilang Liu, Yanis Lahrach, Julia Engdahl, Houston Warren, Darrick Lee, Fabio Ramos, Travis Miles, Ian Abraham

July, 2026 Proceedings of Robotics: Science and Systems (RSS)

Asymptotically Optimal Ergodic Coverage on Generalized Motion Fields

Mandi Zhao, Yifan Hou, Dieter Fox, Yashraj Narang, Ajay Mandlekar, Shuran Song

July, 2026 Proceedings of the International Conference on Machine Learning (ICML)

DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation

Xuning Yang, Rishit Dagli, Alex Zook, Hugo Hadfield, Ankit Goyal, Stan Birchfield, Fabio Ramos, Jonathan Tremblay

July, 2026 Proceedings of Robotics: Science and Systems (RSS)

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

Anthony Liang, Yigit Korkmaz, Jiahui Zhang, Minyoung Hwang, Abrar Anwar, Sid Kaushik, Aditya Shah, Alex S Huang, Luke Zettlemoyer, Dieter Fox, Yu Xiang, Anqi Li, Andreea Bobu, Abhishek Gupta, Stephen Tu, Erdem Biyik, Jesse Zhang

July, 2026 Proceedings of Robotics: Science and Systems (RSS)

Robometer: Scaling Robotic Reward Foundation Models via Preference Prediction

Wenzheng Zhang, Fahira Afzal Maken, Tin Lai, Fabio Ramos