DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training

• URL: http://arxiv.org/abs/2305.12127v1
• Date: Sat, 20 May 2023 07:25:27 GMT
• Title: DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training
• Authors: Aleksei Petrenko, Arthur Allshire, Gavriel State, Ankur Handa, Viktor Makoviychuk
• Abstract summary: We learn dexterous object manipulation using simulated one- or two-armed robots equipped with multi-fingered hand end-effectors. We introduce a decentralized Population-Based Training (PBT) algorithm that allows us to massively amplify the exploration capabilities of deep reinforcement learning.
• Score: 10.808149303943948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose algorithms and methods that enable learning dexterous object manipulation using simulated one- or two-armed robots equipped with multi-fingered hand end-effectors. Using a parallel GPU-accelerated physics simulator (Isaac Gym), we implement challenging tasks for these robots, including regrasping, grasp-and-throw, and object reorientation. To solve these problems we introduce a decentralized Population-Based Training (PBT) algorithm that allows us to massively amplify the exploration capabilities of deep reinforcement learning. We find that this method significantly outperforms regular end-to-end learning and is able to discover robust control policies in challenging tasks. Video demonstrations of learned behaviors and the code can be found at https://sites.google.com/view/dexpbt

Related papers

• SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation [58.14969377419633]
  We propose spire, a system that decomposes tasks into smaller learning subproblems and second combines imitation and reinforcement learning to maximize their strengths. We find that spire outperforms prior approaches that integrate imitation learning, reinforcement learning, and planning by 35% to 50% in average task performance.
  arXiv  Detail & Related papers  (2024-10-23T17:42:07Z)
• Offline Imitation Learning Through Graph Search and Retrieval [57.57306578140857]
  Imitation learning is a powerful machine learning algorithm for a robot to acquire manipulation skills. We propose GSR, a simple yet effective algorithm that learns from suboptimal demonstrations through Graph Search and Retrieval. GSR can achieve a 10% to 30% higher success rate and over 30% higher proficiency compared to baselines.
  arXiv  Detail & Related papers  (2024-07-22T06:12:21Z)
• Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration [8.343657309038285]
  Reinforcement Learning is a powerful framework for developing such robot controllers. We propose a multimodal exploration approach through categorical distributions, which enables us to train planar pushing RL policies. We show that the learned policies are robust to external disturbances and observation noise, and scale to tasks with multiple pushers.
  arXiv  Detail & Related papers  (2023-08-04T16:55:00Z)
• DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality [64.51295032956118]
  We train a policy that can perform robust dexterous manipulation on an anthropomorphic robot hand. Our work reaffirms the possibilities of sim-to-real transfer for dexterous manipulation in diverse kinds of hardware and simulator setups.
  arXiv  Detail & Related papers  (2022-10-25T01:51:36Z)
• Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation [118.27432851053335]
  This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track. The No Interaction track targets for learning policies from pre-collected demonstration trajectories. In this track, we design a Heuristic Rule-based Method (HRM) to trigger high-quality object manipulation by decomposing the task into a series of sub-tasks. For each sub-task, the simple rule-based controlling strategies are adopted to predict actions that can be applied to robotic arms.
  arXiv  Detail & Related papers  (2022-06-13T16:20:42Z)
• Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation [13.135013586592585]
  'Dexterous Made Easy' (DIME) is a new imitation learning framework for dexterous manipulation. DIME only requires a single RGB camera to observe a human operator and teleoperate our robotic hand. On both simulation and real robot benchmarks we demonstrate that DIME can be used to solve complex, in-hand manipulation tasks.
  arXiv  Detail & Related papers  (2022-03-24T17:58:54Z)
• A Framework for Efficient Robotic Manipulation [79.10407063260473]
  We show that a single robotic arm can learn sparse-reward manipulation policies from pixels. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels.
  arXiv  Detail & Related papers  (2020-12-14T22:18:39Z)
• Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks with Base Controllers [26.807673929816026]
  We propose a method of learning long-horizon sparse-reward tasks utilizing one or more traditional base controllers. Our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers.
  arXiv  Detail & Related papers  (2020-11-24T14:23:57Z)
• 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)
• SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks [8.756012472587601]
  Deep reinforcement learning (RL) can be used to learn complex manipulation tasks. RL requires the robot to collect a large amount of real-world experience. S SQUIRL performs a new but related long-horizon task robustly given only a single video demonstration.
  arXiv  Detail & Related papers  (2020-03-10T20:26:26Z)

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.