RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots

• URL: http://arxiv.org/abs/2009.09595v1
• Date: Mon, 21 Sep 2020 03:09:53 GMT
• Title: RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots
• Authors: Tamir Blum, Gabin Paillet, Mickael Laine, Kazuya Yoshida
• Abstract summary: Reinforcement learning (RL) is a promising field to enhance robotic autonomy and decision making capabilities for space robotics. This paper introduces the RL STaR platform, and how researchers can use it through a demonstration.
• Score: 3.249853429482705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) is a promising field to enhance robotic autonomy and decision making capabilities for space robotics, something which is challenging with traditional techniques due to stochasticity and uncertainty within the environment. RL can be used to enable lunar cave exploration with infrequent human feedback, faster and safer lunar surface locomotion or the coordination and collaboration of multi-robot systems. However, there are many hurdles making research challenging for space robotic applications using RL and machine learning, particularly due to insufficient resources for traditional robotics simulators like CoppeliaSim. Our solution to this is an open source modular platform called Reinforcement Learning for Simulation based Training of Robots, or RL STaR, that helps to simplify and accelerate the application of RL to the space robotics research field. This paper introduces the RL STaR platform, and how researchers can use it through a demonstration.

Related papers

• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [85.21378553454672]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment. We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• Learning Bipedal Walking for Humanoids with Current Feedback [5.429166905724048]
  We present an approach for overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion.
  arXiv  Detail & Related papers  (2023-03-07T08:16:46Z)
• A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning [86.06110576808824]
  Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments. Recent advancements in machine learning algorithms and libraries combined with a carefully tuned robot controller lead to learning quadruped in only 20 minutes in the real world.
  arXiv  Detail & Related papers  (2022-08-16T17:37:36Z)
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives [92.0321404272942]
  Reinforcement learning can be used to build general-purpose robotic systems. However, training RL agents to solve robotics tasks still remains challenging. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance.
  arXiv  Detail & Related papers  (2021-10-28T17:59:30Z)
• SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning [78.76052604441519]
  SurRoL is an RL-centered simulation platform for surgical robot learning compatible with the da Vinci Research Kit (dVRK) Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution. We evaluate SurRoL using RL algorithms in simulation, provide in-depth analysis, deploy the trained policies on the real dVRK, and show that our SurRoL achieves better transferability in the real world.
  arXiv  Detail & Related papers  (2021-08-30T07:43:47Z)
• How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned [111.06812202454364]
  We present a number of case studies involving robotic deep RL. We discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting.
  arXiv  Detail & Related papers  (2021-02-04T22:09:28Z)
• Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World [0.0]
  We present a DRL-based algorithm that is capable of performing autonomous robot control using Deep Q-Networks (DQN) In our approach, the agent is trained in a simulated environment and it is able to navigate both in a simulated and real-world environment. The trained agent is able to run on limited hardware resources and its performance is comparable to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-09-23T15:23:54Z)
• robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots [0.5161531917413708]
  We propose an open source toolkit: robo-gym to increase the use of Deep Reinforcement Learning with real robots. We demonstrate a unified setup for simulation and real environments which enables a seamless transfer from training in simulation to application on the robot. We showcase the capabilities and the effectiveness of the framework with two real world applications featuring industrial robots.
  arXiv  Detail & Related papers  (2020-07-06T13:51:33Z)
• Learning to Play Table Tennis From Scratch using Muscular Robots [34.34824536814943]
  This work is the first to (a) fail-safe learn of a safety-critical dynamic task using anthropomorphic robot arms, (b) learn a precision-demanding problem with a PAM-driven system, and (c) train robots to play table tennis without real balls. Videos and datasets are available at muscularTT.embodied.ml.
  arXiv  Detail & Related papers  (2020-06-10T16:43:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.