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Found 8 papers, 3 papers with code
Symmetry Considerations for Learning Task Symmetric Robot Policies
Past methods on encouraging symmetry for robotic tasks have studied this topic mainly in a single-task setting, where symmetry usually refers to symmetry in the motion, such as the gait patterns.
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End
In this paper, we model perception failures as invisible obstacles and pits, and train a reinforcement learning (RL) based local navigation policy to guide our legged robot.
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.
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning
Imitation learning approaches such as adversarial motion priors aim to reduce this problem by encouraging a pre-defined motion style.
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.
