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Found 8 papers, 4 papers with code
DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training
In this work, we propose algorithms and methods that enable learning dexterous object manipulation using simulated one- or two-armed robots equipped with multi-fingered hand end-effectors.
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim.
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Our policies are trained to adapt to a wide range of conditions in simulation.
Factory: Fast Contact for Robotic Assembly
We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics.
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU.
Self-Supervised Real-to-Sim Scene Generation
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.
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning
However, neural network models trained on synthetic data, do not perform well on real data because of the domain gap.
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
Moreover, synthetic SDR data combined with real KITTI data outperforms real KITTI data alone.
