MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation

• URL: http://arxiv.org/abs/2309.14236v2
• Date: Sun, 12 May 2024 13:11:29 GMT
• Title: MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation
• Authors: Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar,
• Abstract summary: MoDem-V2 is capable of learning contact-rich manipulation directly in the uninstrumented real world. We identify key ingredients for leveraging demonstrations in model learning while respecting real-world safety considerations.
• Score: 29.349549097807916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotic systems that aspire to operate in uninstrumented real-world environments must perceive the world directly via onboard sensing. Vision-based learning systems aim to eliminate the need for environment instrumentation by building an implicit understanding of the world based on raw pixels, but navigating the contact-rich high-dimensional search space from solely sparse visual reward signals significantly exacerbates the challenge of exploration. The applicability of such systems is thus typically restricted to simulated or heavily engineered environments since agent exploration in the real-world without the guidance of explicit state estimation and dense rewards can lead to unsafe behavior and safety faults that are catastrophic. In this study, we isolate the root causes behind these limitations to develop a system, called MoDem-V2, capable of learning contact-rich manipulation directly in the uninstrumented real world. Building on the latest algorithmic advancements in model-based reinforcement learning (MBRL), demo-bootstrapping, and effective exploration, MoDem-V2 can acquire contact-rich dexterous manipulation skills directly in the real world. We identify key ingredients for leveraging demonstrations in model learning while respecting real-world safety considerations -- exploration centering, agency handover, and actor-critic ensembles. We empirically demonstrate the contribution of these ingredients in four complex visuo-motor manipulation problems in both simulation and the real world. To the best of our knowledge, our work presents the first successful system for demonstration-augmented visual MBRL trained directly in the real world. Visit https://sites.google.com/view/modem-v2 for videos and more details.

Related papers

• Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation [22.16213887836936]
  Imitation learning methods need human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement learning, on the other hand, can explore the environment autonomously to learn robust behaviors but may require impractical amounts of real-world data collection. We propose RialTo, a system for robustifying real-world imitation learning policies via reinforcement learning in "digital twin" simulation environments constructed on the fly.
  arXiv  Detail & Related papers  (2024-03-06T18:55:36Z)
• Navigating to Objects in the Real World [76.1517654037993]
  We present a large-scale empirical study of semantic visual navigation methods comparing methods from classical, modular, and end-to-end learning approaches. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality.
  arXiv  Detail & Related papers  (2022-12-02T01:10:47Z)
• Bridging the Gap to Real-World Object-Centric Learning [66.55867830853803]
  We show that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing object-centric learning models on simulated data.
  arXiv  Detail & Related papers  (2022-09-29T15:24:47Z)
• Practical Imitation Learning in the Real World via Task Consistency Loss [18.827979446629296]
  This paper introduces a self-supervised loss that encourages sim and real alignment both at the feature and action-prediction levels. We achieve 80% success across ten seen and unseen scenes using only 16.2 hours of teleoperated demonstrations in sim and real.
  arXiv  Detail & Related papers  (2022-02-03T21:43:06Z)
• ReLMM: Practical RL for Learning Mobile Manipulation Skills Using Only Onboard Sensors [64.2809875343854]
  We study how robots can autonomously learn skills that require a combination of navigation and grasping. Our system, ReLMM, can learn continuously on a real-world platform without any environment instrumentation. After a grasp curriculum training phase, ReLMM can learn navigation and grasping together fully automatically, in around 40 hours of real-world training.
  arXiv  Detail & Related papers  (2021-07-28T17:59:41Z)
• Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces [8.692025477306212]
  Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion. We propose a framework combining the learning of a low-dimensional state representation, from high-dimensional observations coming from the robot's raw sensory readings, with the learning of the optimal policy.
  arXiv  Detail & Related papers  (2021-07-04T15:42:01Z)
• Learning a State Representation and Navigation in Cluttered and Dynamic Environments [6.909283975004628]
  We present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments. The robot is able to safely locomote to a target location based on frames from a depth camera without any explicit mapping of the environment. We show that our system can handle noisy depth images, avoid dynamic obstacles unseen during training, and is endowed with local spatial awareness.
  arXiv  Detail & Related papers  (2021-03-07T13:19:06Z)
• ViNG: Learning Open-World Navigation with Visual Goals [82.84193221280216]
  We propose a learning-based navigation system for reaching visually indicated goals. We show that our system, which we call ViNG, outperforms previously-proposed methods for goal-conditioned reinforcement learning. We demonstrate ViNG on a number of real-world applications, such as last-mile delivery and warehouse inspection.
  arXiv  Detail & Related papers  (2020-12-17T18:22:32Z)
• The Ingredients of Real-World Robotic Reinforcement Learning [71.92831985295163]
  We discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world. We propose a particular instantiation of such a system, using dexterous manipulation as our case study. We demonstrate that our complete system can learn without any human intervention, acquiring a variety of vision-based skills with a real-world three-fingered hand.
  arXiv  Detail & Related papers  (2020-04-27T03:36:10Z)
• Visual Navigation Among Humans with Optimal Control as a Supervisor [72.5188978268463]
  We propose an approach that combines learning-based perception with model-based optimal control to navigate among humans. Our approach is enabled by our novel data-generation tool, HumANav. We demonstrate that the learned navigation policies can anticipate and react to humans without explicitly predicting future human motion.
  arXiv  Detail & Related papers  (2020-03-20T16:13:47Z)

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.