reinforcement learning from randomized simulations
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Updated
May 20, 2024 - Python
reinforcement learning from randomized simulations
[ICRA 2024 Workshop] DriveEnv-NeRF: Exploration of A NeRF-Based Autonomous Driving Environment for Real-World Performance Validation
[NeurIPS 2022] 🛒WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
Enabling Faster Training of Robust Reinforcement Learning Policies for Soft Robots
Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots
DROPO: Sim-to-Real Transfer with Offline Domain Randomization
ML Projects and Experience in Industry and Academia.
Simulation environments for the CrazyFlie quadrotor: Used for Reinforcement Learning and Sim-to-Real Transfer
Hiwin Reacher Reinforcement Learning Sim2Real Environment for Omniverse Isaac Gym/Sim
Dofbot Reacher Reinforcement Learning Sim2Real Environment for Omniverse Isaac Gym/Sim
Kuka Reacher Reinforcement Learning Sim2Real Environment for Omniverse Isaac Gym/Sim
UR10 Reacher Reinforcement Learning Sim2Real Environment for Omniverse Isaac Gym/Sim
Representation Abstractions as Incentives for Reinforcement Learning Agents: A Robotic Grasping Case Study
Sim-to-real RL training and deployment tools for the Unitree Go1 robot.
The repository is intended as a support tool for the report of the project "Sim to Real transfer of Reinforcement Learning Policies in Robotics" and it contains examples of some well-known algorithms and methods in the fields of Reinforcement Learning and Sim-to-Real transfer. The implementation is not thought to be efficient, thus we suggest yo…
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning, Project Page: https://leizhang-public.github.io/towards_precise_model_free_robotic_grasping_with_sim_to_real_transfer_learning/
Sim-to-Real Distribution-Aligned Dataset (S2R-DAD) for Domain Shift and Domain Adaptation Analysis
Suite of PyBullet reinforcement learning environments targeted towards using tactile data as the main form of observation.
Simulator, RL-algorithm and firmware for the sim-to-real transfer of drone control policies
🗺🤖🚘🕹📡 An effective, easy-to-implement, and low-cost modular framework for completing complex navigation tasks.
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