Real-World Dexterous Object Manipulation based Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2112.04893v1
• Date: Mon, 22 Nov 2021 02:48:05 GMT
• Title: Real-World Dexterous Object Manipulation based Deep Reinforcement Learning
• Authors: Qingfeng Yao, Jilong Wang, Shuyu Yang
• Abstract summary: We show how to use deep reinforcement learning to control a robot. Our framework reduces the disadvantage of low sample efficiency of deep reinforcement learning. Our algorithm is trained in simulation and migrated to reality without fine-tuning.
• Score: 3.4493195428573613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning has shown its advantages in real-time decision-making based on the state of the agent. In this stage, we solved the task of using a real robot to manipulate the cube to a given trajectory. The task is broken down into different procedures and we propose a hierarchical structure, the high-level deep reinforcement learning model selects appropriate contact positions and the low-level control module performs the position control under the corresponding trajectory. Our framework reduces the disadvantage of low sample efficiency of deep reinforcement learning and lacking adaptability of traditional robot control methods. Our algorithm is trained in simulation and migrated to reality without fine-tuning. The experimental results show the effectiveness of our method both simulation and reality. Our code and video can be found at https://github.com/42jaylonw/RRC2021ThreeWolves and https://youtu.be/Jr176xsn9wg.

Related papers

• LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization [1.1602089225841634]
  This paper introduces LeTO, a method for learning constrained visuomotor policy with differentiable trajectory optimization. We quantitatively evaluate LeTO in simulation and in the real robot.
  arXiv  Detail & Related papers  (2024-01-30T23:18:35Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• DTC: Deep Tracking Control [16.2850135844455]
  We propose a hybrid control architecture that combines the advantages of both worlds to achieve greater robustness, foot-placement accuracy, and terrain generalization. A deep neural network policy is trained in simulation, aiming to track the optimized footholds. We demonstrate superior robustness in the presence of slippery or deformable ground when compared to model-based counterparts.
  arXiv  Detail & Related papers  (2023-09-27T07:57:37Z)
• Obstacle Avoidance for Robotic Manipulator in Joint Space via Improved Proximal Policy Optimization [6.067589886362815]
  In this paper, we train a deep neural network via an improved Proximal Policy Optimization (PPO) algorithm to map from task space to joint space for a 6-DoF manipulator. Since training such a task in real-robot is time-consuming and strenuous, we develop a simulation environment to train the model. Experimental results showed that using our method, the robot was capable of tracking a single target or reaching multiple targets in unstructured environments.
  arXiv  Detail & Related papers  (2022-10-03T10:21:57Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Accelerated Policy Learning with Parallel Differentiable Simulation [59.665651562534755]
  We present a differentiable simulator and a new policy learning algorithm (SHAC) Our algorithm alleviates problems with local minima through a smooth critic function. We show substantial improvements in sample efficiency and wall-clock time over state-of-the-art RL and differentiable simulation-based algorithms.
  arXiv  Detail & Related papers  (2022-04-14T17:46:26Z)
• DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools [96.38972082580294]
  DiffSkill is a novel framework that uses a differentiable physics simulator for skill abstraction to solve deformable object manipulation tasks. In particular, we first obtain short-horizon skills using individual tools from a gradient-based simulator. We then learn a neural skill abstractor from the demonstration trajectories which takes RGBD images as input.
  arXiv  Detail & Related papers  (2022-03-31T17:59:38Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Passing Through Narrow Gaps with Deep Reinforcement Learning [2.299414848492227]
  In this paper we present a deep reinforcement learning method for autonomously navigating through small gaps. We first learn a gap behaviour policy to get through small gaps, where contact between the robot and the gap may be required. In simulation experiments, our approach achieves 93% success rate when the gap behaviour is activated manually by an operator. In real robot experiments, our approach achieves a success rate of 73% with manual activation, and 40% with autonomous behaviour selection.
  arXiv  Detail & Related papers  (2021-03-06T00:10:41Z)
• Neural Dynamic Policies for End-to-End Sensorimotor Learning [51.24542903398335]
  The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space. NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks.
  arXiv  Detail & Related papers  (2020-12-04T18:59:32Z)
• NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control [15.720231070808696]
  In this paper, we propose a novel hierarchical reinforcement learning framework without explicit action. Our meta policy tries to manipulate the next optimal state and actual action is produced by the inverse dynamics model. Under our framework, widely available state-only demonstrations can be exploited effectively for imitation learning.
  arXiv  Detail & Related papers  (2020-11-02T15:28:19Z)

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.