4k citations
- Nvidia (United States)US7 papers
- Stanford UniversityUS7 papers
- University of TorontoCA7 papers
- University of California, MercedUS6 papers
- University of WashingtonUS5 papers
- Duke UniversityUS4 papers
- Massachusetts Institute of TechnologyUS4 papers
- University of CopenhagenDK4 papers
- Google (United States)US3 papers
- Nanyang Technological UniversitySG3 papers
- University of California, BerkeleyUS3 papers
- University of California, IrvineUS3 papers
13 papers · 1 filter
Partial Hierarchical Pose Graph Optimization for SLAM
Alexander Korovko, Dmitry Robustov
In this paper we consider a hierarchical pose graph optimization (HPGO) for Simultaneous Localization and Mapping (SLAM). We propose a fast incremental procedure for building hiera…
CLIPort: What and Where Pathways for Robotic Manipulation
Mohit Shridhar, Lucas Manuelli, Dieter Fox
How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that e…
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…
Rethinking Trajectory Forecasting Evaluation
Boris Ivanovic, Marco Pavone
Forecasting the behavior of other agents is an integral part of the modern robotic autonomy stack, especially in safety-critical scenarios with human-robot interaction, such as aut…
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
Rika Antonova, Fabio Ramos, Rafael Possas +1
BayesSim is a statistical technique for domain randomization in reinforcement learning based on likelihood-free inference of simulation parameters. This paper outlines BayesSimIG:…
STReSSD: Sim-To-Real from Sound for Stochastic Dynamics
Carolyn Matl, Yashraj Narang, Dieter Fox +2
Sound is an information-rich medium that captures dynamic physical events. This work presents STReSSD, a framework that uses sound to bridge the simulation-to-reality gap for stoch…