Real–Sim–Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning
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Summary
Experimental results in two different robot control tasks show that the proposed RSR method can train skill policies with high generalization performance and significantly low training costs.
- Type
- article
- Published
- 2020-02-25
- Cited by
- 41
- References
- 48
- Access
- Open access
- OpenAlex
- https://openalex.org/W3008492644
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:214166097
Keywords
Reinforcement learning, Computer science, Artificial intelligence, Robot, Transfer of learning
References
- Efficient reinforcement learning for robots using informative simulated priors
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Fully convolutional networks for semantic segmentation
- Reinforcement learning in robotics: A survey
- Learning monocular reactive UAV control in cluttered natural environments
- Data-Driven Grasp Synthesis—A Survey
- Reinforcement Learning With Sequences of Motion Primitives for Robust Manipulation
- Neural network Reinforcement Learning for visual control of robot manipulators
- Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning
- Reinforcement Learning in Robotics: Applications and Real-World Challenges
- Human-level control through deep reinforcement learning
- Learning and Reproduction of Gestures by Imitation
- Grasp Planning via Decomposition Trees
- Reinforcement learning in a rule-based navigator for robotic manipulators
- MuJoCo: A physics engine for model-based control
- Deterministic Policy Gradient Algorithms
- Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
- Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
- 3D Simulation for Robot Arm Control with Deep Q-Learning
- (CAD)^2RL: Real Single-Image Flight without a Single Real Image
Cited by
- Efficient Robot Skills Learning with Weighted Near-Optimal Experiences Policy Optimization
- Generalization-Based Acquisition of Training Data for Motor Primitive Learning by Neural Networks
- A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation
- Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements
- Reinforcement learning for robot research: A comprehensive review and open issues
- Towards safe human-to-robot handovers of unknown containers
- Realistic simulation of robotic grasping tasks: review and application
- A model-free deep reinforcement learning approach for control of exoskeleton gait patterns
- Design and intelligent control of mock circulation system to reproduce patient-specific physiological indexes
- Sim–Real Mapping of an Image-Based Robot Arm Controller Using Deep Reinforcement Learning
- Longitudinal deep truck: Deep longitudinal model with application to sim2real deep reinforcement learning for heavy‐duty truck control in the field
- Guided Reinforcement Learning: A Review and Evaluation for Efficient and Effective Real-World Robotics [Survey]
- Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly
- Symmetry-informed surrogates with data-free constraint for real-time acoustic wave propagation
- Fine-Tuning Multimodal Transformer Models for Generating Actions in Virtual and Real Environments
- PLURAL: 3D Point Cloud Transfer Learning via Contrastive Learning With Augmentations
- Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
- A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems
- Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation
- A Twin Delayed Deep Deterministic Policy Gradient Algorithm for Autonomous Ground Vehicle Navigation via Digital Twin Perception Awareness
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