Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments

1 code implementation • 10 Jan 2023 • Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg

We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim.

Imitation Learning Motion Planning +4

RGB-D Local Implicit Function for Depth Completion of Transparent Objects

1 code implementation • CVPR 2021 • Luyang Zhu, Arsalan Mousavian, Yu Xiang, Hammad Mazhar, Jozef van Eenbergen, Shoubhik Debnath, Dieter Fox

Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed.

Depth Completion Depth Estimation +1

Transferable Task Execution from Pixels through Deep Planning Domain Learning

no code implementations • 8 Mar 2020 • Kei Kase, Chris Paxton, Hammad Mazhar, Tetsuya OGATA, Dieter Fox

On the other hand, symbolic planning methods such as STRIPS have long been able to solve new problems given only a domain definition and a symbolic goal, but these approaches often struggle on the real world robotic tasks due to the challenges of grounding these symbols from sensor data in a partially-observable world.