Publications (12)
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…
Self-Supervised Real-to-Sim Scene Generation
Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche +4
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate. Syn…
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Ankur Handa, Arthur Allshire, Viktor Makoviychuk +11
Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to learn complex robotic behaviours in simulation, including in the domain of multi-fingered…
PPISP: Physically-Plausible Compensation and Control of Photometric Variations in Radiance Field Reconstruction
Isaac Deutsch, Nicolas Moënne-Loccoz, Gavriel State +1
Multi-view 3D reconstruction methods remain highly sensitive to photometric inconsistencies arising from camera optical characteristics and variations in image signal processing (I…
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo +8
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy tr…
VoMP: Predicting Volumetric Mechanical Property Fields
Rishit Dagli, Donglai Xiang, Vismay Modi +7
Physical simulation relies on spatially-varying mechanical properties, often laboriously hand-crafted. VoMP is a feed-forward method trained to predict Young's modulus (), Poiss…