Hey everyone!

I'm excited to share UniROS, a ROS-based Reinforcement Learning framework that I've developed to bridge the gap between simulation and real-world robotics. This framework comprises two key packages:

1. MultiROS: Perfect for creating concurrent RL simulation environments using ROS and Gazebo.
2. RealROS: Designed for applying ROS in real robotic environments.

What sets UniROS apart is its ease of transitioning from simulations to real-world applications, making reinforcement learning more accessible and effective for roboticists.

I've also included additional Python bindings for some low-level ROS features, enhancing usability beyond the RL workflow.

I'd love to get your feedback and thoughts on these tools. Let's discuss how they can be applied and improved!

Check them out on GitHub:

• UniROS: github.com/ncbdrck/UniROS
• RealROS: github.com/ncbdrck/realros
• MultiROS: github.com/ncbdrck/multiros

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Hi all I have compiled some study materials and resources to learn RL:

1) Deep RL by Sergey Levine from UC Berkeley 2) David Silver Lecture notes 3) Google Deepmind lecture vids 4) NPTEL IITM Reinforcement Learning

I also prefer the study material to have sufficient mathematical rigour that explains the algos in depth.

Its also intimidating to refer from a bunch of resources at once. Could someone suggest notes and lecture vids from the above listed materials for beginners like me? If you have anyother resources as well do mention them in the comment section.

I'm doing a project that aims to use reinforcement learning (PPO variations) with UAVs. What are the most up to date tools are for implementing and trying new RL algorithms in this space?

I've looked at AirSim, and it seems to no longer be supported by Micrsosoft. I've also been heavily looking at Flightmare, which is almost exactly what I want, but getting the tool that hasn't been maintained for years up and running is giving me headaches (and the documentation is not great/up to date either).

Ultimately, what I'm looking for is: * Physics simulation * Photo-realistic vision * Built-in integration with Gym would be awesome * Python platform preferred, C++ also ok

I've also used ROS/Gazebo with PyTorch previously, and that is my backup plan I suppose, but it's not photo-realistic and is kind of slow in my experience.

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DTC: Deep Tracking Control

Hello. We are the Robotic Systems Lab (RSL) and we research novel strategies for controlling legged robots. In our most recent work, we have combined trajectory optimization with reinforcement learning to synthesize accurate and robust locomotion behaviors.

You can find the ArXiv print here: https://arxiv.org/abs/2309.15462

The method is further described in this video.

We have also demonstrated a potential application for real-world search-and-rescue scenarios in this video.

Hi all,

assuming I give the robot a certain velocity in the x,y,z directions. I want the robot (which has 4dof) to actuate the joints to move the end-effector according to the given velocity.

Currently the observation buffer consists of the joint angle values (4) and the given (3) and the current (3) end-effector velocities. The reward function is defined as:

reward=1/(1+norm(desired_vel, current_vel))

I am using PPO and Isaac GYM. However, the agent is not learning the task at all... Am I missing something?

🌟 Excited to share our recent research, DexCatch!

Pick-and-place is slow and boring, while throw-catching is a behaviour towards more human-like manipulation.

We propose a new model-free framework that can catch diverse objects of daily life with dexterous hands in the air. This ability to catch anything from a cup to a banana, and a pen, can help the hand quickly manipulate objects without transporting objects to their destination -- and even generalize to unseen objects. Video demonstrations of learned behaviors and the code can be found at https://dexcatch.github.io/.

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https://reddit.com/link/17973ri/video/i4xdo39d4lub1/player

Please like,follow and share: Deep Q-Learning to Actor-Critic using Robotics Simulations with Panda-Gym https://medium.com/@andysingal/deep-q-learning-to-actor-critic-using-robotics-simulations-with-panda-gym-ff220f980366

I'm a high school student in an FRC(First robotics competition) team and was looking into using reinforcement learning for our robot. I have some experience in traditional machine learning and we have a CAD of our robot in onshape. I would really appreciate some help on next steps like robot simulation etc...

Edit: We cant pay any subscriptions btw.