mlsanityData to 7 Oct 2026

A Survey of Demonstration Learning

arXiv 2303.11191 ↗First posted 20 Mar 2023cs.LGcited by 12

On the map: Offline RL and imitation learning from data → Robot learning from demonstration

Abstract (arXiv)

With the fast improvement of machine learning, reinforcement learning (RL) has been used to automate human tasks in different areas. However, training such agents is difficult and restricted to expert users. Moreover, it is mostly limited to simulation environments due to the high cost and safety concerns of interactions in the real world. Demonstration Learning is a paradigm in which an agent learns to perform a task by imitating the behavior of an expert shown in demonstrations. It is a relatively recent area in machine learning, but it is gaining significant traction due to having tremendous potential for learning complex behaviors from demonstrations. Learning from demonstration accelerates the learning process by improving sample efficiency, while also reducing the effort of the programmer. Due to learning without interacting with the environment, demonstration learning would allow the automation of a wide range of real world applications such as robotics and healthcare. This paper provides a survey of demonstration learning, where we formally introduce the demonstration problem along with its main challenges and provide a comprehensive overview of the process of learning from demonstrations from the creation of the demonstration data set, to learning methods from demonstrations, and optimization by combining demonstration learning with different machine learning methods. We also review the existing benchmarks and identify their strengths and limitations. Additionally, we discuss the advantages and disadvantages of the paradigm as well as its main applications. Lastly, we discuss our perspective on open problems and research directions for this rapidly growing field.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 116 of its 229 references that are in the map. First posted on arXiv 20 Mar 2023.

Builds on

116 of 229 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Learning from Demonstration with Weakly Supervised Disentanglement16 Jun

Other references · 115

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  5. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  6. 2016Generative Adversarial Imitation Learningcited by 1,576
  7. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  8. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,406
  9. 2019Contrastive Multiview Codingcited by 1,297
  10. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  11. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
  12. 2016End to End Learning for Self-Driving Carscited by 1,000
38 more
  1. 2018DeepMind Control Suitecited by 935
  2. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 882
  3. 2015End-to-End Training of Deep Visuomotor Policiescited by 847
  4. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 833
  5. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 753
  6. 2019Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learningcited by 750
  7. 2020CURL: Contrastive Unsupervised Representations for Reinforcement Learningcited by 745
  8. 2019Behavior Regularized Offline Reinforcement Learningcited by 654
  9. 2020AWAC: Accelerating Online Reinforcement Learning with Offline Datasetscited by 647
  10. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 636
  11. 2020MOPO: Model-based Offline Policy Optimizationcited by 630
  12. 2017Generative Adversarial Networks: An Overviewcited by 588
  13. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  14. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  15. 2017Time-Contrastive Networks: Self-Supervised Learning from Videocited by 453
  16. 2020Reinforcement Learning with Augmented Datacited by 452
  17. 2017Robust Adversarial Reinforcement Learningcited by 439
  18. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 436
  19. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  20. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learningcited by 380
  21. 2020dm_control: Software and Tasks for Continuous Controlcited by 368
  22. 2020Planning to Explore via Self-Supervised World Modelscited by 365
  23. 2021COMBO: Conservative Offline Model-Based Policy Optimizationcited by 365
  24. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 312
  25. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  26. 2016Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learningcited by 285
  27. 2019Improving Sample Efficiency in Model-Free Reinforcement Learning from Imagescited by 268
  28. 2020Decoupling Representation Learning from Reinforcement Learningcited by 263
  29. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 257
  30. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
  31. 2022Masked Visual Pre-training for Motor Controlcited by 246
  32. 2018Safe Exploration in Continuous Action Spacescited by 237
  33. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 228
  34. 2018Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Controlcited by 220
  35. 2020Accelerating Reinforcement Learning with Learned Skill Priorscited by 220
  36. 2019MineRL: A Large-Scale Dataset of Minecraft Demonstrationscited by 210
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  38. 2018RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitationcited by 202

50 most cited of 115 All 115 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 3; 2026: 4.

12 papers.

  1. 2024Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation Systemcited by 13
  2. 2024LLM-driven Imitation of Subrational Behavior : Illusion or Reality?cited by 11
  3. 2024Learning with Language-Guided State Abstractionscited by 11
  4. 2025Learning Algorithms in the Limitcited by 9
  5. 2024Automating Deformable Gasket Assemblycited by 1
  6. 2026SADP: Subgoal-Aware Diffusion Policy for Long-Horizon Manipulation Learned from Foundation Model Generated Demonstrationscited by 1
  7. 2024AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systemscited by 0
  8. 2025Cybernaut: Towards Reliable Web Automationcited by 0
  9. 2025Real-World Robot Control by Deep Active Inference With a Temporally Hierarchical World Modelcited by 0
  10. 2026Actor-Critic Pretraining for Proximal Policy Optimizationcited by 0
  11. 2026RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modescited by 0
  12. 2026Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2019Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
  2. 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
  3. 2020Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
  4. 2021Learning from Imperfect Demonstrations from Agents with Varying Dynamics
  5. 2022Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning
  6. 2021Imitation Learning: Progress, Taxonomies and Challenges
  7. 2024"Give Me an Example Like This": Episodic Active Reinforcement Learning from Demonstrations
  8. 2018An Algorithmic Perspective on Imitation Learning