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Found 9 papers, 4 papers with code
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.
Advanced Skills by Learning Locomotion and Local Navigation End-to-End
It is free to select its path and the locomotion gait.
Neural Scene Representation for Locomotion on Structured Terrain
We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments.
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
In this work, we present and study a training set-up that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU.
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.
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation
A kitchen assistant needs to operate human-scale objects, such as cabinets and ovens, in unmapped environments with dynamic obstacles.
Learning a State Representation and Navigation in Cluttered and Dynamic Environments
We show that decoupling the pipeline into these components results in a sample efficient policy learning stage that can be fully trained in simulation in just a dozen minutes.
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago).
Deep Value Model Predictive Control
The DMPC actor is a Model Predictive Control (MPC) optimizer with an objective function defined in terms of a value function estimated by the critic.
