On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning

• URL: http://arxiv.org/abs/2104.14534v1
• Date: Thu, 29 Apr 2021 17:49:20 GMT
• Title: On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning
• Authors: Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco and Daniele Pucci
• Abstract summary: We apply model-free Deep Reinforcement Learning for training a general and robust humanoid push-recovery policy in a simulation environment. Our method targets high-dimensional whole-body humanoid control and is validated on the iCub humanoid.
• Score: 32.070068456106895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successful in specific scenarios, this approach requires demanding tuning of parameters and switching logic between specifically-designed controllers for handling more general perturbations. We apply model-free Deep Reinforcement Learning for training a general and robust humanoid push-recovery policy in a simulation environment. Our method targets high-dimensional whole-body humanoid control and is validated on the iCub humanoid. Reward components incorporating expert knowledge on humanoid control enable fast learning of several robust behaviors by the same policy, spanning the entire body. We validate our method with extensive quantitative analyses in simulation, including out-of-sample tasks which demonstrate policy robustness and generalization, both key requirements towards real-world robot deployment.

Related papers

• Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills [10.43221469116584]
  We propose Meta-Control, which creates customized state representations and control strategies tailored to specific tasks. Our core insight is that a meta-control system can be built to automate the thought process that human experts use to design control systems.
  arXiv  Detail & Related papers  (2024-05-18T19:58:44Z)
• Twisting Lids Off with Two Hands [82.21668778600414]
  We show how policies trained in simulation can be effectively and efficiently transferred to the real world. Specifically, we consider the problem of twisting lids of various bottle-like objects with two hands. This is the first sim-to-real RL system that enables such capabilities on bimanual multi-fingered hands.
  arXiv  Detail & Related papers  (2024-03-04T18:59:30Z)
• 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)
• SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation [10.349135207285464]
  We show that modular control via muscle synergies enables organisms to learn muscle control in a simplified and generalizable action space. We use physiologically accurate human hand and leg models as a testbed for determining the extent to which a Synergistic Action Representation (SAR) acquired from simpler tasks facilitates learning more complex tasks. We find in both cases that SAR-exploiting policies significantly outperform end-to-end reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-07T17:07:41Z)
• Learning a Universal Human Prior for Dexterous Manipulation from Human Preference [35.54663426598218]
  We propose a framework that learns a universal human prior using direct human preference feedback over videos. A task-agnostic reward model is trained through iteratively generating diverse polices and collecting human preference over the trajectories. Our method empirically demonstrates more human-like behaviors on robot hands in diverse tasks including even unseen tasks.
  arXiv  Detail & Related papers  (2023-04-10T14:17:33Z)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• Adversarial Training is Not Ready for Robot Learning [55.493354071227174]
  Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations. We show theoretically and experimentally that neural controllers obtained via adversarial training are subjected to three types of defects. Our results suggest that adversarial training is not yet ready for robot learning.
  arXiv  Detail & Related papers  (2021-03-15T07:51:31Z)
• Learning Compliance Adaptation in Contact-Rich Manipulation [81.40695846555955]
  We propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller.
  arXiv  Detail & Related papers  (2020-05-01T05:23:34Z)
• Learning Whole-body Motor Skills for Humanoids [25.443880385966114]
  This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors. The policy is trained in a physics simulator with realistic setting of robot model and low-level impedance control that are easy to transfer the learned skills to real robots.
  arXiv  Detail & Related papers  (2020-02-07T19:40:59Z)

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.