Overcoming Exploration in Reinforcement Learning with Demonstrations

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Teaching NICO How to Grasp: An Empirical Study on Crossmodal Social Interaction as a Key Factor for Robots Learning From Humans

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Learning From Oracle Demonstrations—A New Approach to Develop Autonomous Intersection Management Control Algorithms Based on Multiagent Deep Reinforcement Learning

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Dynamic Experience ReplayPreprint

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Asymmetric self-play for automatic goal discovery in robotic manipulation

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Demonstration-Guided Reinforcement Learning with Learned SkillsPreprint

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Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations

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Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models

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Constrained-Space Optimization and Reinforcement Learning for Complex Tasks

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Episodic Self-Imitation Learning with Hindsight

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JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning

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Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance

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TRASS: Time Reversal as Self-Supervision

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Imitation-Learning-Enabled Vehicular Edge Computing: Toward Online Task Scheduling

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Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study

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A Confrontation Decision-Making Method with Deep Reinforcement Learning and Knowledge Transfer for Multi-Agent System

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Hindsight Goal Ranking on Replay Buffer for Sparse Reward Environment

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TAG: Teacher-Advice Mechanism With Gaussian Process for Reinforcement Learning

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Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality

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A reliable traversability learning method based on human-demonstrated risk cost mapping for mobile robots over uneven terrain

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From Imitation to Refinement - Residual Rl for Precise Assembly

Ankile, L., Simeonov, A., Shenfeld, I., Torne, M., & Agrawal, P. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127442 · Cited by 75

Transferring Human Manipulation Knowledge to Industrial Robots Using Reinforcement Learning

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A conceptual framework for externally-influenced agents: an assisted reinforcement learning review

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Policy Gradient From Demonstration and Curiosity

Chen, J., & Xu, W. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3150802 · Cited by 16

Learning latent state representation for speeding up explorationPreprint

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Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short SurveyPreprint

Colas, C., Karch, T., Sigaud, O., & Oudeyer, P. (2021) · HAL · Cited by 16

Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control

Dawood, M., Dengler, N., de Heuvel, J., & Bennewitz, M. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161492 · Cited by 14

Hundreds Guide Millions: Adaptive Offline Reinforcement Learning With Expert Guidance

Yang, Q., Wang, S., Zhang, Q., Huang, G. M., & Song, S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3293508 · Cited by 14

Learning agility and adaptive legged locomotion via curricular hindsight reinforcement learning

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Generating attentive goals for prioritized hindsight reinforcement learning

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Human-Inspired Multi-Agent Navigation using Knowledge Distillation

Xu, P., & Karamouzas, I. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636463 · Cited by 15

Active Inference Integrated With Imitation Learning for Autonomous Driving

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Deep Reinforcement Learning-Based Control for Stomach Coverage Scanning of Wireless Capsule Endoscopy

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Skill-based Programming of Force-controlled Assembly Tasks using Deep Reinforcement Learning

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Watch, Try, Learn: Meta-Learning from Demonstrations and RewardPreprint

Zhou, A., Jang, E., Kappler, D., Herzog, A., Khansari, M., Wohlhart, P., Bai, Y., Kalakrishnan, M., Levine, S., & Finn, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03352 · Cited by 31

Offline Learning from Demonstrations and Unlabeled ExperiencePreprint

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Residual Reinforcement Learning from DemonstrationsPreprint

Alakuijala, M., Dulac-Arnold, G., Mairal, J., Ponce, J., & Schmid, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08050 · Cited by 14

AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at ScalePreprint

Lu, Y., Hausman, K., Chebotar, Y., Yan, M., Jang, E., Herzog, A., Xiao, T. G., Irpan, A., Khansari, M., Kalashnikov, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05424 · Cited by 20

Knowledge-Guided Reinforcement Learning with Artificial Potential Field-Based Demonstrations for Multi-Autonomous Underwater Vehicle Cooperative Hunting

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Deep Reinforcement Learning for Peg-in-hole Assembly Task Via Information Utilization Method

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RLfOLD: Reinforcement Learning from Online Demonstrations in Urban Autonomous Driving

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Multi-step Pick-and-Place Tasks Using Object-centric Dense Correspondences

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Comparison of Two Reinforcement Learning Algorithms for Robotic Pick and Place with Non-Visual Sensing

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Hierarchical Learning from Demonstrations for Long-Horizon Tasks

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Batch Prioritization in Multigoal Reinforcement Learning

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Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point CloudsPreprint

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Robotics: Science and Systems XV

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Deep Reinforcement Learning for Robotic Control with Multi-Fidelity Models

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End-to-End Learning of Deep Visuomotor Policy for Needle Picking

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FoX: Formation-Aware Exploration in Multi-Agent Reinforcement Learning

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Deep imitative reinforcement learning with gradient conflict-free for decision-making in autonomous vehicles

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Hybrid Imitation Learning Framework for Robotic Manipulation Tasks

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Curiosity-Driven Multi-Criteria Hindsight Experience ReplayPreprint

Lanier, J. B., McAleer, S., & Baldi, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03710 · Cited by 13

Co-training for Policy LearningPreprint

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Watch, Try, Learn: Meta-Learning from Demonstrations and RewardsPreprint

Zhou, A., Jang, E., Kappler, D., Herzog, A., Khansari, M., Wohlhart, P., Bai, Y., Kalakrishnan, M., Levine, S., & Finn, C. (2020) · arXiv (Cornell University) · Cited by 12

A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future DirectionsPreprint

Mondal, A. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.06921 · Cited by 13

Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement LearningPreprint

Charlesworth, H. J., & Montana, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05104 · Cited by 12

Continuous Control with Action Quantization from DemonstrationsPreprint

Dadashi, R., Hussenot, L., Vincent, D., Girgin, S., Raichuk, A., Geist, M., & Pietquin, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10149 · Cited by 15

Reinforcement learning in robotic systems : A review on sim-to-real transfer

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Deep Reinforcement Learning Task Assignment Based on Domain Knowledge

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Hierarchical Reinforcement Learning from Demonstration via Reachability-Based Reward Shaping

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Compliant Motion Planning Integrating Human Skill for Robotic Arm Collecting Tomato Bunch Based on Improved DDPG

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Data-driven and model-based hybrid reinforcement learning to reduce stress on power systems branches

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Manipulation skill learning on multi-step complex task based on explicit and implicit curriculum learning

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Evolutionary Action Selection for Gradient-Based Policy Learning

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RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement Learning

Ceola, F., Rosasco, L., & Natale, L. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3363532 · Cited by 11

Lucid dreaming for experience replay: refreshing past states with the current policy

Du, Y., Warnell, G., Gebremedhin, A. H., Stone, P., & Taylor, M. E. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06104-5 · Cited by 11

[CASPI] Causal-aware Safe Policy Improvement for Task-oriented Dialogue

Ramachandran, G. S., Hashimoto, K., & Xiong, C. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.8 · Cited by 22

Automata Guided Reinforcement Learning With DemonstrationsPreprint

Li, X., Ma, Y., & Belta, C. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06305 · Cited by 11

OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and researchPreprint

Kumar, A., Buckley, T., Lanier, J. B., Wang, Q., Kavelaars, A. T., & Kuzovkin, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.08639 · Cited by 16

Safely Learn to Fly Aircraft From Human: An Offline–Online Reinforcement Learning Strategy and Its Application to Aircraft Stall Recovery

Jiang, H., Xiong, H., Zeng, W., & Ou, Y. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3299913 · Cited by 10

Hierarchical reinforcement learning from imperfect demonstrations through reachable coverage-based subgoal filtering

Tang, Y. F., Guo, S., Liu, J., Wan, B., An, L., & Liu, J. K. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111736 · Cited by 10

Blockchain-based crowdsourced deep reinforcement learning as a service

Alagha, A., Otrok, H., Singh, S., Mizouni, R., & Bentahar, J. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121107 · Cited by 10

Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objects

Mosbach, M., & Behnke, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610700 · Cited by 10

Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions

Al-Sharman, M. K., Edes, L., Sun, B., Jayakumar, V., Tahir, H., Daoud, M., Emran, B., Rayside, D., & Melek, W. W. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3646982 · Cited by 20

Hierarchical learning from human preferences and curiosity

Bougie, N., & Ichise, R. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02726-3 · Cited by 10

Robotic Embodiment of Human-Like Motor Skills via Reinforcement Learning

Guzman, L., Morellas, V., & Papanikolopoulos, N. P. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3147453 · Cited by 10

Adaptive exploration policy for exploration–exploitation tradeoff in continuous action control optimization

Li, M., Huang, T., & Zhu, W. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01387-5 · Cited by 10

CSGP: Closed-Loop Safe Grasp Planning via Attention-Based Deep Reinforcement Learning From Demonstrations

Tang, Z., Shi, Y., & Xu, X. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3253023 · Cited by 10

HER-PDQN: A Reinforcement Learning Approach for UAV Navigation with Hybrid Action Spaces and Sparse Rewards

Liu, C., & Kampen, E.-J. V. (2022) · AIAA SciTech Forum · doi:10.2514/6.2022-0793 · Cited by 10

Time Reversal as Self-SupervisionPreprint

Nair, S., Babaeizadeh, M., Finn, C., Levine, S., & Kumar, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.01128 · Cited by 10

Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RLPreprint

Kartal, B., Hernández-Leal, P., & Taylor, M. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00045 · Cited by 12

Bridging the Imitation Gap by Adaptive InsubordinationPreprint

Weihs, L., Jain, U., Liu, I.-J., Salvador, J., Lazebnik, S., Kembhavi, A., & Schwing, A. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.12173 · Cited by 16

Align-RUDDER: Learning From Few Demonstrations by Reward RedistributionPreprint

Patil, V. P., Hofmarcher, M., Dinu, M.-C., Dorfer, M., Blies, P., Brandstetter, J., Arjona-Medina, J. A., & Hochreiter, S. (2020) · arXiv (Cornell University) · doi:10.13140/rg.2.2.16583.32161 · Cited by 32

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference LearningPreprint

Filos, A., Lyle, C., Gal, Y., Levine, S., Jaques, N., & Farquhar, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.12560 · Cited by 12

Reinforcement Learning in Quantitative Trading: A SurveyPreprint

Alameer, A., Saleh, H., & Alshehri, K. A. (2022) · doi:10.36227/techrxiv.19303853 · Cited by 10

Model-based trajectory stitching for improved behavioural cloning and its applications

Hepburn, C. A., & Montana, G. (2023) · Machine Learning · doi:10.1007/s10994-023-06392-z · Cited by 8

Soft imitation reinforcement learning with value decomposition for portfolio management

Dong, L., & Zheng, H.-C. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111108 · Cited by 10

SC-AIRL: Share-Critic in Adversarial Inverse Reinforcement Learning for Long-Horizon Task

Xiang, G., Li, S., Shuang, F., Gao, F., & Yuan, X. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3366023 · Cited by 9

Safety Decision-Making for Autonomous Vehicles Integrating Passenger Physiological States by fNIRS

Zhang, X., Zheng, H., Li, J., Xie, Z., Sun, H., & Wang, H. (2024) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0205 · Cited by 13

Autonomous PID Tuning: Two-Phase Reinforcement Learning Through Adversarial Imitation Learning Under Imperfect Demonstrations

Kim, G., Chang, K., Byun, Y., & Baek, J. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3600504 · Cited by 9

Self-Punishment and Reward Backfill for Deep Q-Learning

Bonyadi, M. R., Wang, R., & Ziaei, M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3140042 · Cited by 9

A Grasp Pose is All You Need: Learning Multi-Fingered Grasping with Deep Reinforcement Learning from Vision and Touch

Ceola, F., Maiettini, E., Rosasco, L., & Natale, L. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341776 · Cited by 9

Potential Driven Reinforcement Learning for Hard Exploration Tasks

Zhao, E., Deng, S., Zang, Y., Kang, Y., Li, K., & Xing, J. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/290 · Cited by 9

A self-guided approach for navigation in a minimalistic foraging robotic swarm

Adams, S., Ornia, D. J., & Mazo, M. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10102-y · Cited by 9

A data-efficient goal-directed deep reinforcement learning method for robot visuomotor skill

Jiang, R., Wang, Z., He, B., Zhou, Y., Li, G., & Zhu, Z. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.023 · Cited by 9

Multi-Agent Advisor Q-Learning

Subramanian, S. G., Taylor, M. E., Larson, K. S., & Crowley, M. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13445 · Cited by 9

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., & Riedmiller, M. (2017) · arXiv · doi:10.48550/arxiv.1707.08817 · Cited by 478

Efficient hindsight reinforcement learning using demonstrations for robotic tasks with sparse rewards

Zuo, G., Zhao, Q., Lu, J., & Li, J. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881419898342 · Cited by 22

Learning from Demonstration without Demonstrations

Blau, T., Morere, P., & Francis, G. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561119 · Cited by 3

Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning

Yang, X., Ji, Z., Wu, J., & Lai, Y. (2022) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac55051.2022.9911100 · Cited by 6

Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningPreprint

Escoriza, A. L., Hansen, N., Tao, S., Mu, T., & Su, H. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2503.01837 · Cited by 6