Cited by 712Reinforcement Learning in RoboticsRobot Manipulation and LearningDomain Adaptation and Few-Shot Learning

The DOI matches its Crossref record; 40 of the 44 references it cites were found in a registry or an index.

Overcoming Exploration in Reinforcement Learning with Demonstrations

Open access

Nair, Ashvin, McGrew, Bob, Andrychowicz, Marcin, Zaremba, Wojciech, Abbeel, Pieter·2018·2018 IEEE International Conference on Robotics and Automation (ICRA)

DOI 10.1109/icra.2018.8463162

Open this paper in CoChat — free account Open PDF

Abstract

Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and manually shaping a reward function can result in suboptimal performance. However, finding a non-zero reward is exponentially more difficult with increasing task horizon or action dimensionality. This puts many real-world tasks out of practical reach of RL methods. In this work, we use demonstrations to overcome the exploration problem and successfully learn to perform long-horizon, multi-step robotics tasks with continuous control such as stacking blocks with a robot arm. Our method, which builds on top of Deep Deterministic Policy Gradients and Hindsight Experience Replay, provides an order of magnitude of speedup over RL on simulated robotics tasks. It is simple to implement and makes only the additional assumption that we can collect a small set of demonstrations. Furthermore, our method is able to solve tasks not solvable by either RL or behavior cloning alone, and often ends up outperforming the demonstrator policy.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/icra.2018.8463162

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343 · cited 1×
“More recent work on task and motion planning considers both logical and physical aspects of the task [27], [28], [29], but requires domainspecific engineering.”
Background
MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., & Tassa, Y. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6386109 · Cited by 4,767
Trust region policy optimization
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578
Self-citation
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198

Flagged first, then by citations

Show all 44 references

4 of the 44 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

hester, 2017, Learning from demonstrations for real world reinforcement learning
ng, 2000, Algorithms for Inverse Reinforcement Learning, International Conference on Machine Learning (ICML)
ziebart, 2008, Maximum Entropy Inverse Reinforcement Learning, AAAI Conference on Artificial Intelligence, 1433

Sorted by citations

Items 401–500 of 710

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot ManipulationPreprint
Liu, I.-C. A., Uppal, S., Sukhatme, G. S., Lim, J. J., Englert, P. A. J., & Lee, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.06383 · Cited by 5
Proceedings of the Cardiff University Engineering Research Conference 2023
Stucchi, N. M. E., Nardi, C. D., Franceschin, G., Ronchin, L., Vavasori, A., & Traviglia, A. (2024) · doi:10.18573/conf1 · Cited by 5
Behavior Cloning Assisted Reinforcement Learning for Cable-Driven Continuum Space Robots in Sparse Reward Environments
Tian, X., Xia, B., Tan, J., Yuan, B., Li, Z., & Wang, X. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3588411 · Cited by 4
Adversarial Reinforcement Learning for Enhanced Decision-Making of Evacuation Guidance Robots in Intelligent Fire Scenarios
Zhao, H.-T., Liang, Z.-H., Ma, T.-X., Shi, X., Kapadia, M., Thrash, T., Hoelscher, C., Jia, J.-Y., Liu, B., & Cao, J.-X. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3502420 · Cited by 6
MILE: Model-Based Intervention Learning
Korkmaz, Y., & Bıyık, E. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128018 · Cited by 4
Generative Exploration and Exploitation
Jiang, J., & Lu, Z. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5858 · Cited by 5
Actor-critic reinforcement learning for bidding in bilateral negotiation
Arslan, F., & Aydoğan, R. (2022) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.3899 · Cited by 4
An Open Tele-Impedance Framework to Generate Data for Contact-Rich Tasks in Robotic Manipulation
Giammarino, A., Gandarías, J. M., & Ajoudani, A. (2023) · IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) · doi:10.1109/arso56563.2023.10187436 · Cited by 4
From mimic to counteract: a two-stage reinforcement learning algorithm for Google research football
Zhao, J., Lin, J., Zhang, X., Li, Y., Zhou, X., & Sun, Y. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09455-x · Cited by 5
Composable Interaction Primitives: A Structured Policy Class for Efficiently Learning Sustained-Contact Manipulation Skills
Abbatematteo, B., Rosen, E., Thompson, S., Akbulut, T., Rammohan, S., & Konidaris, G. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610846 · Cited by 4
Deep Reinforcement Learning for Quantitative Trading
Xu, M., Lan, Z.-X., Tao, Z., Du, J.-W., & Ye, Z. (2023) · 2024 4th International Conference on Electronics, Circuits and Information Engineering (ECIE) · doi:10.1109/ecie61885.2024.10626209 · Cited by 27
R $$\times $$ R: Rapid eXploration for Reinforcement learning via sampling-based reset distributions and imitation pre-training
Khandate, G., Saidi, T. L., Shang, S., Chang, E. T., Liu, Y., Dennis, S., Adams, J., & Ciocarlie, M. (2024) · Autonomous Robots · doi:10.1007/s10514-024-10170-8 · Cited by 4
Generalize Robot Learning From Demonstration to Variant Scenarios With Evolutionary Policy Gradient
Cao, J., Liu, W., Liu, Y., & Yang, J. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.00021 · Cited by 4
Transfer Learning with Demonstration Forgetting for Robotic Manipulator
Aitygulov, E., & Panov, A. I. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.04.159 · Cited by 4
Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning
Hong, Z.-W., Nagarajan, P., & Maeda, G. J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86486-6_6 · Cited by 4
SOZIL: Self-Optimal Zero-Shot Imitation Learning
Peng, H., Lü, T., Cui, S., Wei, J., Cai, Y., & Wang, S. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3116604 · Cited by 4
Re-exploration of ε-Greedy in Deep Reinforcement Learning
Amin, M. R. R. M., & Othman, M. F. (2021) · Lecture notes in mechanical engineering · doi:10.1007/978-981-16-4803-8_27 · Cited by 4
Quantum-enhanced reinforcement learning for control: a preliminary study
Hu, Y., Tang, F., Chen, J., & Wang, W. (2021) · Control Theory and Technology · doi:10.1007/s11768-021-00063-x · Cited by 6
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control
Qiu, Y., Jin, Y., Wang, J., & Zhang, X. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945272 · Cited by 4
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets
Cai, Y., Zhang, C., Zhao, L., Shen, W., Zhang, X., Song, L., Bian, J., Qin, T., & Liu, T. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00012 · Cited by 4
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policy Optimization
Chakraborty, S., Bedi, A. S., Weerakoon, K., Poddar, P., Koppel, A., Tokekar, P., & Manocha, D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161186 · Cited by 4
Combining imitation and deep reinforcement learning to human-level performance on a virtual foraging task
Giammarino, V., Dunne, M. F., Moore, K. N., Hasselmo, M. E., Stern, C. E., & Paschalidis, I. C. (2023) · Adaptive Behavior · doi:10.1177/10597123231201655 · Cited by 4
Sample-Efficient Real-Time Planning with Curiosity Cross-Entropy Method and Contrastive Learning
Kotb, M., Weber, C., & Wermter, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342018 · Cited by 6
Exploration Decay Policy (EDP) to Enhanced Exploration-Exploitation Trade-Off in DDPG for Continuous Action Control Optimization
Sumiea, E. H., Abdulkadir, S. J., Alhussian, H., Al-Selwi, S. M., Ragab, M. G., & Alqushaibi, A. (2023) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored60679.2023.10563810 · Cited by 4
Online Hybrid Learning to Speed Up Deep Reinforcement Learning Method for Commercial Aircraft Control
Xin, M., Gao, Y., Mou, T., & Ye, J. (2019) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isass.2019.8757756 · Cited by 4
An Automatic Robot Skills Learning System from Robot’s Real-World Demonstrations
Li, B., Lu, T., Li, X., Cai, Y., & Wang, S. (2019) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2019.8833143 · Cited by 4
Learning visual policies for building 3D shape categories
Pashevich, A., Kalevatykh, I., Ivan Laptev, A., & Schmid, C. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341750 · Cited by 4
Automated Generation of Robot Trajectories for Assembly Processes Requiring Only Sparse Manual Input
Madsen, S., Jami, M., & Petersen, H. G. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561465 · Cited by 4
Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills
Razmjoo, A., Lembono, T. S., & Calinon, S. (2021) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar53236.2021.9659381 · Cited by 4
Unmanned Aerial Vehicles Path Planning Based on Deep Reinforcement Learning
Wang, G., Zheng, X., Zhao, H., Zhao, Q., Zhang, C., & Zhang, B. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-32456-8_9 · Cited by 4
Addressing Reward Engineering for Deep Reinforcement Learning on Multi-stage Task
Chen, B., & Su, J. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-36802-9_33 · Cited by 4
Reinforced Imitation in Heterogeneous Action SpacePreprint
Żołna, K., Rostamzadeh, N., Bengio, Y., Ahn, S., & Pinheiro, P. O. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03438 · Cited by 10
Combining learned skills and reinforcement learning for robotic manipulations.Preprint
Strudel, R., Pashevich, A., Kalevatykh, I., Laptev, I., Šivic, J., & Schmid, C. (2019) · arXiv (Cornell University) · Cited by 4
Interpretable Policy Specification and Synthesis through Natural Language and RLPreprint
Tambwekar, P., Silva, A., Gopalan, N., & Gombolay, M. (2021) · arXiv (Cornell University) · Cited by 4
SCAPE: Learning Stiffness Control from Augmented Position Control ExperiencesPreprint
Kim, M.-C., Niekum, S., & Deshpande, A. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08442 · Cited by 4
Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force LearningPreprint
Wang, Y., Beltran-Hernandez, C. C., Wan, W., & Harada, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.05912 · Cited by 4
Reinforcement Learning in Quantitative Trading: A SurveyPreprint
Alameer, A., Saleh, H., & Alshehri, K. A. (2022) · doi:10.36227/techrxiv.19303853.v1 · Cited by 6
Improving Offline Reinforcement Learning with Inaccurate Simulators
Hou, Y., Sun, H., Ma, J., & Wu, F. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610833 · Cited by 3
An effective exploration method based on N-step updated Dirichlet distribution and Dempster–Shafer theory for deep reinforcement learning
Huang, F., He, Y., Zhang, Y., Chen, B., & Yang, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110443 · Cited by 3
SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch
Feng, S., & Yang, Y. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i11.33219 · Cited by 4
Help Me Through: Imitation Learning Based Active View Planning to Avoid SLAM Tracking Failures
Naveed, K., Hussain, W., Hussain, I., Lee, D., & Anjum, M. L. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3582817 · Cited by 3
A single-demonstration guided manipulation learning with dexterous hand
Li, J., Lv, Y., Lin, X., Hang, J., Li, X., & Sun, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111606 · Cited by 3
Continuous-time hierarchical reinforcement learning for satellite pursuit decision
Linsen, W. E. I., Xin, N. I. N. G., Lian, X., Wang, F., Zhang, G., & Lin, M. (2025) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2025.103662 · Cited by 3
Sustainable Reinforcement Learning for Autonomous Driving Under Postsuspension of Human Guidance
Dai, L., Zhang, C., Zhang, H., Ji, Y., & Yan, H. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3605349 · Cited by 3
Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains
Xu, J., Yin, K., Chen, Z., Gregory, J. M., Stump, E., & Liu, L. (2024) · The International Journal of Robotics Research · doi:10.1177/02783649231225977 · Cited by 3
A deep reinforcement learning control method guided by RBF-ARX pseudo LQR
Peng, T., Peng, H., & Liu, F. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02283-4 · Cited by 3
Thinking Before Decision: Efficient Interactive Visual Navigation Based on Local Accessibility Prediction
Liu, Q., Luo, B., Zhang, D., & Chen, R. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3522769 · Cited by 3
Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning
Dantas, J. P. A., Máximo, M. R. O. A., & Yoneyama, T. (2025) · IEEE Access · doi:10.1109/access.2025.3567186 · Cited by 3
Imitation-Reinforcement Learning Penetration Strategy for Hypersonic Vehicle in Gliding Phase
Lei, X., Guan, Y., Pu, J., & Wei, C. (2025) · Aerospace · doi:10.3390/aerospace12050438 · Cited by 3
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
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration
Chen, Z., Chen, B., Xie, S., Gong, L., Liu, C., Zhang, Z., & Zhang, J. (2021) · International Symposium on Robotics & Intelligent Manufacturing Technology (ISRIMT) · doi:10.1109/isrimt53730.2021.9597110 · Cited by 3
Implementing Robotic Pick and Place with Non-visual Sensing Using Reinforcement Learning
Imtiaz, M. B., Qiao, Y., & Lee, B. (2022) · International Conference on Robotics, Control and Automation (ICRCA) · doi:10.1109/icrca55033.2022.9828993 · Cited by 6
A reinforcement learning algorithm acquires demonstration from the training agent by dividing the task space
Zu, L., He, X., Yang, J. N., Liu, L., & Wang, W. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.04.042 · Cited by 3
Reinforcement Learning with Reward Shaping and Hybrid Exploration in Sparse Reward Scenes
Yang, Y., Cao, W., Guo, L., Gan, C., & Wu, M. (2023) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps58381.2023.10128012 · Cited by 4
Using Learning Curve Predictions to Learn from Incorrect Feedback
Faulkner, T. K., & Thomaz, A. L. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161105 · Cited by 3
Deep Reinforcement Learning with Implicit Imitation for Lane-Free Autonomous Driving
Chrysomallis, I., Troullinos, D., Chalkiadakis, G., Papamichail, I., & Papageorgiou, M. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230304 · Cited by 5
Achieving Goals Using Reward Shaping and Curriculum Learning
Anca, M., Thomas, J. V., Pedamonti, D., Hansen, M. H., & Studley, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47454-5_24 · Cited by 3
Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method
Liu, W., Feng, Q., Xiao, S., & Li, H. (2024) · IEEE Access · doi:10.1109/access.2024.3405961 · Cited by 3
Deep Reinforcement Learning Based Intelligent Resource Allocation in Hybrid Vehicle Scenario
Lou, C., Hou, F., Li, B., & Ding, H. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3483891 · Cited by 4
``Give Me an Example Like This'': Episodic Active Reinforcement Learning from Demonstrations
Hou, M., Hindriks, K. V., Eiben, A. E., & Baraka, K. (2024) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3687272.3688298 · Cited by 3
Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
Jansonnie, P., Wu, B., Pérez, J., & Peters, J. (2024) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids58906.2024.10769879 · Cited by 3
DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots
Bauzá, M., Chen, J. E., Dalibard, V., Gileadi, N., Hafner, R., Martins, M. F., Moore, J., Pevceviciute, R., Laurens, A., Rao, D., et al. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127813 · Cited by 20
Learning Visual Servo Policies via Planner Cloning
Viereck, U., Saenko, K., & Platt, R. (2021) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-71151-1_26 · Cited by 2
Demonstration Guided Actor-Critic Deep Reinforcement Learning for Fast Teaching of Robots in Dynamic Environments
Gong, L., Sun, T., Li, X., Lin, K. C., Díaz-Rodríguez, N., Filliat, D., Zhang, Z., & Zhang, J. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.04.227 · Cited by 3
LIDAR: learning from imperfect demonstrations with advantage rectification
Zhang, X., Ma, H., Luo, X., & Yuan, J. (2021) · Frontiers of Computer Science · doi:10.1007/s11704-021-0147-9 · Cited by 3
Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations.Preprint
Thananjeyan, B., Balakrishna, A., Rosolia, U., Li, F., McAllister, R., Gonzalez, J. E., Levine, S., Borrelli, F., & Goldberg, K. Y. (2019) · arXiv (Cornell University) · Cited by 3
Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional SystemsPreprint
Meisheri, H., Baniwal, V., Sultana, N. N., Ravindran, B., & Khadilkar, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00211 · Cited by 4
Flexible and Efficient Long-Range Planning Through Curious ExplorationPreprint
Curtis, A., Xin, M., Arumugam, D., Feigelis, K., & Yamins, D. L. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10876 · Cited by 3
Learning to Play by Imitating HumansPreprint
Dinyari, R., Sermanet, P., & Lynch, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06874 · Cited by 6
Learning Abstract Models for Strategic Exploration and Fast Reward TransferPreprint
Liu, E. Z., Keramati, R., Seshadri, S., Guu, K., Pasupat, P., Brunskill, E., & Liang, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05896 · Cited by 7
Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined GoalsPreprint
Kilinc, O., & Montana, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02066 · Cited by 3
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture SearchPreprint
Fu, Y., Yu, Z., Zhang, Y., & Lin, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.13091 · Cited by 6
Causal-aware Safe Policy Improvement for Task-oriented dialoguePreprint
Ramachandran, G. S., Hashimoto, K., & Xiong, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.06370 · Cited by 3
Reinforced Imitation Learning by Free Energy PrinciplePreprint
Ogishima, R., Karino, I., & Kuniyoshi, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11811 · Cited by 4
On Pathologies in KL-Regularized Reinforcement Learning from Expert DemonstrationsPreprint
Rudner, T. G. J., Lu, C., Osborne, M. A., Gal, Y., & Teh, Y. W. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.13936 · Cited by 3
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
Tomar, M., Sathuluri, A., & Ravindran, B. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.330110053 · Cited by 2
Skill enhancement learning with knowledge distillation
Liu, N., Sun, F., Fang, B., & Liu, H. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-4016-0 · Cited by 2
Accelerating Virtual Fixture Estimation for Robot Manipulation using Reinforcement Learning and Human Demonstrations
Prado, D. F., Larintzakis, K., Irsperger, J., & Steinbach, E. G. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711706 · Cited by 2
Enhancing DRL-based lane change with rule-integrated sampling
Liu, Q., Zhang, J., Lian, R., Lu, J., Tian, Y., Li, Z., Wang, X., Li, X., Ge, J., & Li, L. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105297 · Cited by 2
From Simulation to Reality: Deep Reinforcement Learning for Autonomous Underwater Vehicle Docking
Bharti, V., Tunçay, S., Carlucho, I., Koskinopoulou, M., & Pétillot, Y. (2025) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans58557.2025.11104520 · Cited by 2
Intelligent Critic Design With Policy Transfer for Wastewater Treatment Processes
Wang, D., Gao, N., Li, X., Ye, K., & Qiao, J. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3620541 · Cited by 2
Accelerating Residual Reinforcement Learning With Uncertainty Estimation
Dodeja, L., Schmeckpeper, K., Vats, S., Weng, T., Jia, M., Konidaris, G., & Tellex, S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3636808 · Cited by 2
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL
Akinola, I., Wang, Z., & Allen, P. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636369 · Cited by 2
Goal-driven active learning
Bougie, N., & Ichise, R. (2021) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-021-09527-5 · Cited by 3
Solving sparse reward games using deep Q-learning with demonstration and partial training
Halim, A. M., Margaretha, H., Saputra, K. V. I., & Lukas, S. (2021) · AIP conference proceedings · doi:10.1063/5.0075682 · Cited by 2
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination
Mohtasib, A., Neumann, G. M., & Cuayáhuitl, H. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981056 · Cited by 2
Heuristic Reward Function for Reinforcement Learning Based Manipulator Motion Planning
Zhang, J., Guo, J., & Bai, C. (2022) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus55513.2022.9986816 · Cited by 2
Redundant robot control with learning from expert demonstrations
Ramírez, J., & Yu, W. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022138 · Cited by 2
Research on Robotic Arm Based on YOLO
Li, H., Zhou, S., Du, Y., Zou, Q., & Tang, S. (2022) · International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI) · doi:10.1109/ahpcai57455.2022.10087753 · Cited by 2
Learning Diverse Policies with Soft Self‐Generated Guidance
Wang, G., Wu, F., Zhang, X., & Liu, J. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/4705291 · Cited by 2
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning
Castagna, A., & Dusparić, I. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230291 · Cited by 2
Deep-reinforcement-learning-based robot motion strategies for grabbing objects from human hands
Cai, Z., Feng, Z., Zhou, L., Yang, X., & Xu, T. (2023) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.12.001 · Cited by 2
Conditional neural processes for model-based reinforcement learning with stability guarantees
JiaNan, Y. A. N. G., Ding, Y., Zhu, Y., Bo, C. A. I., YuTing, M. A., YunPeng, L. I., & MingHao, H. A. N. (2023) · Scientia Sinica Technologica · doi:10.1360/sst-2022-0321 · Cited by 2
Trajectory Generation Using Dual-Robot Haptic Interface for Reinforcement Learning from Demonstration
Frau-Alfaro, D., Méndez, S. T. P., & Páez-Ubieta, I. D. L. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-58676-7_36 · Cited by 2
Offline Reinforcement Learning with Failure Under Sparse Reward Environments
Wu, M., Siddique, U., Sinha, A., & Cao, Y. (2024) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi60790.2024.10585936 · Cited by 2
WayEx: Waypoint Exploration using a Single Demonstration
Levy, M., Saini, N., & Shrivastava, A. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611088 · Cited by 2
Demo2Test: Transfer Testing of Agent in Competitive Environment with Failure Demonstrations
Jian-ming, C., Wang, Y., Wang, J., Xie, X., Wang, D., Wang, Q., & Xu, F. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3696001 · Cited by 2
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning
Jiang, Y., Yang, Y., Lan, Z., Zhan, G., Li, S. E., Sun, Q., Ma, J., Yu, T., & Zhang, C. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801306 · Cited by 2
Signal Temporal Logic-Guided Apprenticeship Learning
Puranic, A. G., Deshmukh, J. V., & Nikolaidis, S. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801924 · Cited by 2
Sparse Reward Based Manipulator Motion Planning by Using High Speed Learning from Demonstrations
Zuo, G., Lu, J., & Pan, T. (2018) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2018.8665328 · Cited by 2

Authors: Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., & Abbeel, P.
Affiliations: OpenAI (United States) (US)
ORCID: Abbeel, Pieter
Venue: 2018 IEEE International Conference on Robotics and Automation (ICRA)
Publisher: IEEE
Published: 2018-05
Pages: 6292-6299
ISSN: 1050-4729 (ISSN-L)
DOI: 10.1109/icra.2018.8463162
OpenAlex: W2756826236
Cited by: 712
References: 44
Citations by year: 2026: 24 · 2025: 72 · 2024: 86 · 2023: 111 · 2022: 93 · 2021: 117 · 2020: 105 · 2019: 67 · 2018: 35
FWCI: 33.08
Type: conference paper
Language: en
Open access: green
arxiv.org
Locations: Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: exploration in reinforcement learning, sparse rewards, demonstrations, deep deterministic policy gradient, hindsight experience replay, long-horizon tasks, continuous control, robotic manipulation, block stacking, robot manipulators, behavior cloning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Overcoming Exploration in Reinforcement Learning with Demonstrations

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks