38 citations · 104 across the 33 of their papers we have counts for
38 papers
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models
Changyeon Kim, Kimin Lee, Yashraj Narang +1
Reinforcement learning (RL) post-training improves robot foundation models (RFMs) beyond their demonstrations, but real-world deployment demands more: learning new tasks efficientl…
FlashNeRD: Performance-First Contact-Rich Neural Robot Dynamics
Mohammadmehdi Ataei, Mohammad Amin Nabian, Jie Xu +9
Compared with analytical physics, learned dynamics models promise robot simulation that is faster, inherently differentiable, and easily adaptable to real data. Neural Robot Dynami…
Generalizable Robotic Insertion with World Models
Nicklas Hansen, Iretiayo Akinola, Yijie Guo +7
Robotic assembly in high-mixture settings requires adaptable systems that can handle diverse parts, yet current approaches typically rely on policies specialized to each insertion…
PAKT: Physically-Aligned Kinesthetic Teaching for Reinforcement Learning
Lars Johannsmeier, Yashraj Narang
Real-world reinforcement learning (RL) systems still struggle with the demands of contact-rich industrial manipulation, including micrometer-level precision, success rates above 99…
SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
Stone Tao, Jie Xu, Hesam Rabeti +3
Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes. Prior data-driven approaches, particularly imitation learnin…
ROSA: A Robotics Foundation Model Serving System for Robot Factories
Wenqi Jiang, Jason Clemons, Rowland O'Flaherty +5
Robotics foundation models (RFMs) are making general-purpose robots increasingly practical for factory deployments. While RFM serving systems are central to this vision, existing s…
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…
SPARR: Simulation-based Policies with Asymmetric Real-world Residuals for Assembly
Yijie Guo, Iretiayo Akinola, Lars Johannsmeier +3
Robotic assembly presents a long-standing challenge due to its requirement for precise, contact-rich manipulation. While simulation-based learning has enabled the development of ro…
Point Bridge: 3D Representations for Cross Domain Policy Learning
Siddhant Haldar, Lars Johannsmeier, Lerrel Pinto +4
Robot foundation models are beginning to deliver on the promise of generalist robotic agents, yet progress remains constrained by the scarcity of large-scale real-world manipulatio…
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA, :, Mayank Mittal +104
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab c…
World Simulation with Video Foundation Models for Physical AI
NVIDIA, :, Arslan Ali +87
We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2…
The Reality Gap in Robotics: Challenges, Solutions, and Best Practices
Elie Aljalbout, Jiaxu Xing, Angel Romero +9
Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driv…
Refinery: Active Fine-tuning and Deployment-time Optimization for Contact-Rich Policies
Bingjie Tang, Iretiayo Akinola, Jie Xu +6
Simulation-based learning has enabled policies for precise, contact-rich tasks (e.g., robotic assembly) to reach high success rates (~80%) under high levels of observation noise an…
Neural Robot Dynamics
Jie Xu, Eric Heiden, Iretiayo Akinola +3
Accurate and efficient simulation of modern robots remains challenging due to their high degrees of freedom and intricate mechanisms. Neural simulators have emerged as a promising…
RobotSmith: Generative Robotic Tool Design for Acquisition of Complex Manipulation Skills
Chunru Lin, Haotian Yuan, Yian Wang +7
Endowing robots with tool design abilities is critical for enabling them to solve complex manipulation tasks that would otherwise be intractable. While recent generative frameworks…
DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
Zhao Mandi, Yifan Hou, Dieter Fox +3
We study the problem of functional retargeting: learning dexterous manipulation policies to track object states from human hand-object demonstrations. We focus on long-horizon, bim…
MatchMaker: Automated Asset Generation for Robotic Assembly
Yian Wang, Bingjie Tang, Chuang Gan +4
Robotic assembly remains a significant challenge due to complexities in visual perception, functional grasping, contact-rich manipulation, and performing high-precision tasks. Simu…
SRSA: Skill Retrieval and Adaptation for Robotic Assembly Tasks
Yijie Guo, Bingjie Tang, Iretiayo Akinola +3
Enabling robots to learn novel tasks in a data-efficient manner is a long-standing challenge. Common strategies involve carefully leveraging prior experiences, especially transitio…
One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation
Zhendong Wang, Zhaoshuo Li, Ajay Mandlekar +9
Diffusion models, praised for their success in generative tasks, are increasingly being applied to robotics, demonstrating exceptional performance in behavior cloning. However, the…
FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
Michael Noseworthy, Bingjie Tang, Bowen Wen +7
We present FORGE, a method for sim-to-real transfer of force-aware manipulation policies in the presence of significant pose uncertainty. During simulation-based policy learning, F…
AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries
Bingjie Tang, Iretiayo Akinola, Jie Xu +7
Robotic assembly for high-mixture settings requires adaptivity to diverse parts and poses, which is an open challenge. Meanwhile, in other areas of robotics, large models and sim-t…
Benchmarking Population-Based Reinforcement Learning across Robotic Tasks with GPU-Accelerated Simulation
Asad Ali Shahid, Yashraj Narang, Vincenzo Petrone +5
In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks. However, this comes at the cost of an enormous amount of…
MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Ajay Mandlekar, Soroush Nasiriany, Bowen Wen +5
Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely c…
IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality
Bingjie Tang, Michael A. Lin, Iretiayo Akinola +5
Robotic assembly is a longstanding challenge, requiring contact-rich interaction and high precision and accuracy. Many applications also require adaptivity to diverse parts, poses,…