ReviewSensors (Basel, Switzerland)2021
Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Deep learning based personalized tourism recommendation for behavior analysis and route optimization.Scientific reports · 2025Article
- Deep Reinforcement Learning of Mobile Robot Navigation in Dynamic Environment: A Review.Sensors (Basel, Switzerland) · 2025Review
- Article
- An intelligent fault diagnosis model for bearings with adaptive hyperparameter tuning in multi-condition and limited sample scenarios.Scientific reports · 2025Article
- Segment, Compare, and Learn: Creating Movement Libraries of Complex Task for Learning from Demonstration.Biomimetics (Basel, Switzerland) · 2025Article
- Sensorimotor delays constrain robust locomotion in a 3D kinematic model of fly walking.bioRxiv : the preprint server for biology · 2025Article
- Affinity-Driven Transfer Learning for Load Forecasting.Sensors (Basel, Switzerland) · 2024Article
- Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review.Sensors (Basel, Switzerland) · 2024Review
- An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.Sensors (Basel, Switzerland) · 2024Article
- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- FPGA Implementation of Image Registration Using Accelerated CNN.Sensors (Basel, Switzerland) · 2023Article
- Biomimetic Approaches for Human Arm Motion Generation: Literature Review and Future Directions.Sensors (Basel, Switzerland) · 2023Review
- A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation.Sensors (Basel, Switzerland) · 2023Review
- Robot Programming from a Single Demonstration for High Precision Industrial Insertion.Sensors (Basel, Switzerland) · 2023Article
- Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment.Sensors (Basel, Switzerland) · 2023Article
- Repetition-Based Approach for Task Adaptation in Imitation Learning.Sensors (Basel, Switzerland) · 2022Article
- DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution.Sensors (Basel, Switzerland) · 2022Article
- Pedestrian and Animal Recognition Using Doppler Radar Signature and Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment.Micromachines · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.