NVIDIA at CoRL 2024
November 6–9
Science Congress Center Munich
Munich, Germany
Discover What’s Possible With NVIDIA Robotics
The Conference on Robot Learning (CoRL) is an annual international conference focusing on the intersection of robotics and machine learning. Explore the work to see how NVIDIA Research collaborates with CoRL members to deliver AI breakthroughs across the community.
News and Announcements at CoRL 2024
NVIDIA Research at CoRL
-
Papers
-
Workshops
NVIDIA’s accepted papers at CoRL 2024 feature a range of groundbreaking research in the field of robotics. From humanoids to policy, explore the work NVIDIA is bringing to the CoRL community.
*Denotes equal contribution to the paper.
ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation
Abrar Anwar, John Welsh, Joydeep Biswas, Soha Pouya, Yan Chang
SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment
Caelan Garrett*, Ajay Mandlekar*, Bowen Wen, Dieter Fox
Avoid Everything: Model-Free Collision Avoidance With Expert-Guided Fine-Tuning
Adam Fishman, Aaron Walsman, Mohak Bhardwaj, Wentao Yuan, Balakumar Sundaralingam, Byron Boots, Dieter Fox
RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics
Wentao Yuan, Jiafei Duan, Valts Blukis, Wilbert Pumacay, Ranjay Krishna, Adithyavairavan Murali, Arsalan Mousavian, Dieter Fox
DiffusionSeeder: Seeding Motion Optimization With Diffusion for Rapid Motion Planning
Raven Huang, Balakumar Sundaralingam, Arsalan Mousavian, Adithyavairavan Murali, Ken Goldberg, Dieter Fox
NOD-TAMP: Generalizable Long-Horizon Planning With Neural Object Descriptors
Shuo Cheng, Caelan Garrett*, Ajay Mandlekar*, Danfei Xu
Discovering Robotic Interaction Modes With Discrete Representation Learning
Liquan Wang, Ankit Goyal, Haoping Xu, Animesh Garg
Manipulate-Anything: Automating Real-World Robots Using Vision-Language Models
Jiafei Duan, Wentao Yuan, Wilbert Pumacay, Yi Ru Wang, Kiana Ehsani, Dieter Fox, Ranjay Krishna
DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping With Geometric Fabrics
Tyler Ga Wei Lum*, Martin Matak*, Viktor Makoviychuk, Ankur Handa, Arthur Allshire, Tucker Hermans, Nathan D. Ratliff*, Karl Van Wyk*
Multi-Task Interactive Robot Fleet Learning With Visual World Models
Huihan Liu, Yu Zhang, Vaarij Betala, Evan Zhang, James Liu, Crystal Ding, Yuke Zhu
Harmon: Whole-Body Motion Generation of Humanoid Robots From Language Descriptions
Zhenyu Jiang, Yuqi Xie, Jinhan Li, Ye Yuan, Yifeng Zhu, Yuke Zhu
OKAMI: Teaching Humanoid Robots Manipulation Skills Through Single Video Imitation
Jinhan Li, Yifeng Zhu, Yuqi Xie, Zhenyu Jiang, Mingyo Seo, Georgios Pavlakos, Yuke Zhu
SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation
Zihan Zhou, Animesh Garg, Dieter Fox, Caelan Reed Garrett, Ajay Mandlekar
Gentle Manipulation of Tree Branches: A Contact-Aware Policy Learning Approach
Jay Jacob, Shizhe Cai, Paulo Vinicius Koerich Borges, Tirthankar Bandyopadhyay, Fabio Ramos
RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
Yuxuan Kuang, Junjie Ye, Haoran Geng, Jiageng Mao, Congyue Deng, Leonidas Guibas, He Wang, Yue Wang
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving
Thomas Tian, Boyi Li, Xinshuo Weng, Yuxiao Chen, Edward Schmerling, Yue Wang, Boris Ivanovic, Marco Pavone
Promptable Closed-Loop Traffic Generation
Shuhan Tan, Boris Ivanovic, Yuxiao Chen, Boyi Li, Xinshuo Weng, Yulong Cao, Philipp Kraehenbuehl, Marco Pavone
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation
Jean Pierre Sleiman, Mayank Mittal, Marco Hutter
NVIDIA Research
Discover our most recent AI research and the new capabilities deep learning brings to visual and audio applications. Explore the latest innovations and see how you can bring them into your own work.
Explore NVIDIA Robotics Solutions
Isaac ROS
NVIDIA Isaac™ ROS is built on the open-source ROS 2 software framework. This means the millions of developers in the ROS community can take advantage of NVIDIA-accelerated libraries and AI models to accelerate their development and deployment workflows.
Isaac Perceptor
Isaac Perceptor is a workflow built on Isaac ROS that lets you quickly build robust autonomous mobile robots (AMRs) that can perceive, localize, and operate in unstructured environments like warehouses or factories.
Isaac Manipulator
The Isaac Manipulator workflow is built on Isaac ROS, letting you build AI-enabled robot arms—or manipulators—that can seamlessly perceive, understand, and interact with their environments.
Isaac Sim
NVIDIA Isaac Sim™ is a reference application enabling developers to design, simulate, test, and train AI-based robots and autonomous machines in a physically based virtual environment.
NVIDIA Robotics Developer Resources and Community
Documentation
Explore the NVIDIA Isaac ROS document hub for key resources, like a getting started guide, reference workflows, packages, and more.
Training
NVIDIA offers extensive robotics training and resources to enhance your expertise in AI and related technologies.
Forums
Dive into the NVIDIA robotics community to connect, ask questions, and receive expert advice and tailored recommendations.
Livestream
Participate in our regular robotics livestreams to interact with a vibrant community of developers and industry experts.
Like No Place You’ve Ever Worked
Working at NVIDIA, you’ll solve some of the world’s hardest problems and discover never-before-seen ways to improve the quality of life for people everywhere. From healthcare to robots, self-driving cars to blockbuster movies, you’ll experience it all. Plus, there’s a growing list of new opportunities every single day. Explore all of our open roles, including internships and new college graduate positions.
Learn more about our current job openings, as well as university jobs.
Sign up to receive the latest news from NVIDIA.