mlsanityData to 7 Oct 2026

Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review

arXiv 2102.04148 ↗First posted 8 Feb 2021cs.ROcited by 34

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Abstract (arXiv)

Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named reinforcement learning, tries to find an optimal behavior strategy through interactions with the environment. Combining deep learning and reinforcement learning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals, such as robotic manipulation and control tasks, that neither method permits resolving when applied on its own. In this paper, we present recent significant progress of deep reinforcement learning algorithms, which try to tackle the problems for the application in the domain of robotic manipulation control, such as sample efficiency and generalization. Despite these continuous improvements, currently, the challenges of learning robust and versatile manipulation skills for robots with deep reinforcement learning are still far from being resolved for real world applications.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 6; 2024: 10; 2025: 7; 2026: 1.

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  1. 2022Sample Efficient Deep Reinforcement Learning via Uncertainty Estimationcited by 26
  2. 2024Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulationcited by 22
  3. 2023Ensemble Reinforcement Learning: A Surveycited by 16
  4. 2024General-purpose foundation models for increased autonomy in robot-assisted surgerycited by 15
  5. 2025Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learningcited by 14
  6. 2022DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learningcited by 8
  7. 2021Solving the Real Robot Challenge using Deep Reinforcement Learningcited by 5
  8. 2022Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Taskscited by 5
  9. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  10. 2021ACERAC: Efficient reinforcement learning in fine time discretizationcited by 4
  11. 2024Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agentcited by 3
  12. 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challengescited by 3
22 more
  1. 2023Machine Learning Meets Advanced Robotic Manipulationcited by 2
  2. 2024Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in AI, Machine Learning, and Digital Twincited by 2
  3. 2024Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learningcited by 2
  4. 2024MOSEAC: Streamlined Variable Time Step Reinforcement Learningcited by 2
  5. 2024Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Controlcited by 2
  6. 2025Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learningcited by 2
  7. 2025Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Surveycited by 2
  8. 2025Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learningcited by 2
  9. 2022Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Trainingcited by 1
  10. 2022Query The Agent: Improving sample efficiency through epistemic uncertainty estimationcited by 1
  11. 2023Hierarchical Needs-driven Agent Learning Systems: From Deep Reinforcement Learning To Diverse Strategiescited by 1
  12. 2023Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scorescited by 1
  13. 2023Actor-Critic with variable time discretization via sustained actionscited by 1
  14. 2021Surgical task expertise detected by a self-organizing neural network mapcited by 0
  15. 2022Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learningcited by 0
  16. 2022Automatic Evaluation of Excavator Operators using Learned Reward Functionscited by 0
  17. 2023SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Modelscited by 0
  18. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Taskscited by 0
  19. 2024Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancementcited by 0
  20. 2024GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Modelcited by 0
  21. 2025Intelligent Control of Spacecraft Reaction Wheel Attitude Using Deep Reinforcement Learningcited by 0
  22. 2026Multi-Agent Reinforcement Learning via Agent-Specific Preferencecited by 0