ReviewSensors (Basel, Switzerland)2023
A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Vision-Based Human-Robot Handover System with Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
- Prediction of Composite Supercapacitor Performance Through Combining Machine Learning with Novel Binder-Related Features.Nanomaterials (Basel, Switzerland) · 2026Article
- A methodological framework and experimental protocol for proactive human-robot collaboration with multimodal intention prediction and adaptive control.Frontiers in robotics and AI · 2026Article
- Ontology-driven integration of advertised and operational capabilities in robots.Scientific reports · 2025Article
- DT-HRL: Mastering Long-Sequence Manipulation with Reimagined Hierarchical Reinforcement Learning.Biomimetics (Basel, Switzerland) · 2025Article
- Intelligent generation and optimization of resources in music teaching reform based on artificial intelligence and deep learning.Scientific reports · 2025Article
- Robotic Arm Trajectory Planning in Dynamic Environments Based on Self-Optimizing Replay Mechanism.Sensors (Basel, Switzerland) · 2025Article
- The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence.Scientific reports · 2025Article
- Reinforcement Learning Approach to Optimizing Profilometric Sensor Trajectories for Surface Inspection.Sensors (Basel, Switzerland) · 2025Article
- Compliant Motion Planning Integrating Human Skill for Robotic Arm Collecting Tomato Bunch Based on Improved DDPG.Plants (Basel, Switzerland) · 2025Article
- Multi-robot hierarchical safe reinforcement learning autonomous decision-making strategy based on uniformly ultimate boundedness constraints.Scientific reports · 2025Article
- Interactive imitation learning for dexterous robotic manipulation: challenges and perspectives-a survey.Frontiers in robotics and AI · 2025Review
- Survey of learning-based approaches for robotic in-hand manipulation.Frontiers in robotics and AI · 2024Review
- Learning Playing Piano with Bionic-Constrained Diffusion Policy for Anthropomorphic Hand.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor-critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.
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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.