The first gym-gazebo was a successful proof of concept, which is being used by multiple research laboratories and many users of the robotics community. Given its positive impact, specially regarding usability, researchers at Acutronic Robotics have now freshly launched体育馆2.
“This is the logical evolution towards our initial goal: to bring RL methods into robotics at a professional and industrial level.” — Risto Kojcev, head of AI, Acutronic Robotics
他领导的AI团队研究了如何使用加强学习（RL）而不是传统的路径规划技术。
“We aim to train behaviors that can be applied in complex dynamic environments, which resemble the new demands of agile production and human robot collaboration scenarios.”
Achieving this would lead to faster and easier development of robotic applications and moving the RL techniques from a research setting to a production environment. gym-gazebo2 is a step forward in this long-term goal.
纸，可用这里，展示了升级的，现实世界中的，以应用程序为导向的版本的Gym Gazebo，基于Ros-和Gazebo的RL Toolkit，符合Openai的健身房。
The text discusses the newROS 2基于基于的软件体系结构并总结了使用近端策略优化（PPO）获得的结果。最终，这项工作的输出为机器人技术提供了基准测试系统，该系统允许使用相同的虚拟条件比较不同的技术和算法。
团队专注于玛拉, a modular robotic arm that is natively running ROS 2 in each of its modules. They have evaluated four different environments with different levels of complexity of MARA, reaching accuracies in the millimeter scale. The environments are MARA, MARA Orient, MARA Collision, and MARA Collision Orient.
“We have focused on MARA first for being this modular robot arm the most direct option of transferring policies learned in gym-gazebo2 to the real world, hopefully industrial applications.”
The converged results show the feasibility and usefulness of the gym-gazebo 2 toolkit, its potential and applicability in industrial use cases, using modular robots.
更多资源：
- 同一AI团队也提出了ROS2Learn, a novel reinforcement learning framework for ROS 2
- 体育馆2教程
- 说明installation
- 更多usage
KeynotesEditor’s note:This post is republished fromAcutronic Robotics.
Filed Under:The Robot Report
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