IRASim: Learning Interactive Real-Robot Action Simulators

• URL: http://arxiv.org/abs/2406.14540v1
• Date: Thu, 20 Jun 2024 17:50:16 GMT
• Title: IRASim: Learning Interactive Real-Robot Action Simulators
• Authors: Fangqi Zhu, Hongtao Wu, Song Guo, Yuxiao Liu, Chilam Cheang, Tao Kong,
• Abstract summary: We introduce a novel method, IRASim, to generate realistic videos of a robot arm that executes a given action trajectory. To validate the effectiveness of our method, we create a new benchmark, IRASim Benchmark, based on three real-robot datasets. Results show that IRASim outperforms all the baseline methods and is more preferable in human evaluations.
• Score: 24.591694756757278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scalable robot learning in the real world is limited by the cost and safety issues of real robots. In addition, rolling out robot trajectories in the real world can be time-consuming and labor-intensive. In this paper, we propose to learn an interactive real-robot action simulator as an alternative. We introduce a novel method, IRASim, which leverages the power of generative models to generate extremely realistic videos of a robot arm that executes a given action trajectory, starting from an initial given frame. To validate the effectiveness of our method, we create a new benchmark, IRASim Benchmark, based on three real-robot datasets and perform extensive experiments on the benchmark. Results show that IRASim outperforms all the baseline methods and is more preferable in human evaluations. We hope that IRASim can serve as an effective and scalable approach to enhance robot learning in the real world. To promote research for generative real-robot action simulators, we open-source code, benchmark, and checkpoints at https: //gen-irasim.github.io.

Related papers

• Simulation-Aided Policy Tuning for Black-Box Robot Learning [47.83474891747279]
  We present a novel black-box policy search algorithm focused on data-efficient policy improvements. The algorithm learns directly on the robot and treats simulation as an additional information source to speed up the learning process. We show fast and successful task learning on a robot manipulator with the aid of an imperfect simulator.
  arXiv  Detail & Related papers  (2024-11-21T15:52:23Z)
• Generalized Robot Learning Framework [10.03174544844559]
  We present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots.
  arXiv  Detail & Related papers  (2024-09-18T15:34:31Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes [5.80109297939618]
  We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies.
  arXiv  Detail & Related papers  (2023-06-27T16:59:15Z)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• DayDreamer: World Models for Physical Robot Learning [142.11031132529524]
  Deep reinforcement learning is a common approach to robot learning but requires a large amount of trial and error to learn. Many advances in robot learning rely on simulators. In this paper, we apply Dreamer to 4 robots to learn online and directly in the real world, without simulators.
  arXiv  Detail & Related papers  (2022-06-28T17:44:48Z)
• REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer [57.045140028275036]
  We consider the problem of transferring a policy across two different robots with significantly different parameters such as kinematics and morphology. Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being mismatched in different robots. We propose a novel method named $REvolveR$ of using continuous evolutionary models for robotic policy transfer implemented in a physics simulator.
  arXiv  Detail & Related papers  (2022-02-10T18:50:25Z)
• Back to Reality for Imitation Learning [8.57914821832517]
  Imitation learning, and robot learning in general, emerged due to breakthroughs in machine learning, rather than breakthroughs in robotics. We believe that a better metric for real-world robot learning is time efficiency, which better models the true cost to humans.
  arXiv  Detail & Related papers  (2021-11-25T02:03:52Z)
• 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)

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.