Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks

• URL: http://arxiv.org/abs/2206.06282v1
• Date: Mon, 13 Jun 2022 16:12:56 GMT
• Title: Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
• Authors: Josip Josifovski, Mohammadhossein Malmir, Noah Klarmann, Bare Luka \v{Z}agar, Nicol\'as Navarro-Guerrero and Alois Knoll
• Abstract summary: We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation.
• Score: 2.018504891256636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this problem, we define an easy-to-reproduce experimental setup for a robotic reach-and-balance manipulator task, which can serve as a benchmark for comparison. We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation. Fully randomized simulations and fine-tuning show differentiated results and translate better to the real robot than the other approaches tested.

Related papers

• EAGERx: Graph-Based Framework for Sim2real Robot Learning [9.145895178276822]
  Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics. We introduce EAGERx, a framework with a unified software pipeline for both real and simulated robot learning.
  arXiv  Detail & Related papers  (2024-07-05T08:01:19Z)
• DrEureka: Language Model Guided Sim-To-Real Transfer [64.14314476811806]
  Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. In this paper, we investigate using Large Language Models (LLMs) to automate and accelerate sim-to-real design. Our approach is capable of solving novel robot tasks, such as quadruped balancing and walking atop a yoga ball.
  arXiv  Detail & Related papers  (2024-06-04T04:53:05Z)
• DiffGen: Robot Demonstration Generation via Differentiable Physics Simulation, Differentiable Rendering, and Vision-Language Model [72.66465487508556]
  DiffGen is a novel framework that integrates differentiable physics simulation, differentiable rendering, and a vision-language model. It can generate realistic robot demonstrations by minimizing the distance between the embedding of the language instruction and the embedding of the simulated observation. Experiments demonstrate that with DiffGen, we could efficiently and effectively generate robot data with minimal human effort or training time.
  arXiv  Detail & Related papers  (2024-05-12T15:38:17Z)
• Learning Quadruped Locomotion Using Differentiable Simulation [31.80380408663424]
  Differentiable simulation promises fast convergence and stable training. This work proposes a new differentiable simulation framework to overcome these challenges. Our framework enables learning quadruped walking in simulation in minutes without parallelization.
  arXiv  Detail & Related papers  (2024-03-21T22:18:59Z)
• Robust Visual Sim-to-Real Transfer for Robotic Manipulation [79.66851068682779]
  Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One common approach to bridge the visual sim-to-real domain gap is domain randomization (DR)
  arXiv  Detail & Related papers  (2023-07-28T05:47:24Z)
• Robot Learning from Randomized Simulations: A Review [59.992761565399185]
  Deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. State-of-the-art approaches learn in simulation where data generation is fast as well as inexpensive. We focus on a technique named 'domain randomization' which is a method for learning from randomized simulations.
  arXiv  Detail & Related papers  (2021-11-01T13:55:41Z)
• Auto-Tuned Sim-to-Real Transfer [143.44593793640814]
  Policies trained in simulation often fail when transferred to the real world. Current approaches to tackle this problem, such as domain randomization, require prior knowledge and engineering. We propose a method for automatically tuning simulator system parameters to match the real world.
  arXiv  Detail & Related papers  (2021-04-15T17:59:55Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)

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.