mlsanityData to 7 Oct 2026

Learning Robot Skills with Temporal Variational Inference

arXiv 2006.16232 ↗First posted 29 Jun 2020cs.LG cs.RO stat.MLcited by 54

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Abstract (arXiv)

In this paper, we address the discovery of robotic options from demonstrations in an unsupervised manner. Specifically, we present a framework to jointly learn low-level control policies and higher-level policies of how to use them from demonstrations of a robot performing various tasks. By representing options as continuous latent variables, we frame the problem of learning these options as latent variable inference. We then present a temporal formulation of variational inference based on a temporal factorization of trajectory likelihoods,that allows us to infer options in an unsupervised manner. We demonstrate the ability of our framework to learn such options across three robotic demonstration datasets.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2020.

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Citing papers per year: 2020: 1; 2021: 6; 2022: 15; 2023: 12; 2024: 12; 2025: 6; 2026: 2.

54 papers.

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  1. 2024RT-H: Action Hierarchies Using Languagecited by 204
  2. 2020OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learningcited by 164
  3. 2025Reinforcement Learning with Action Chunkingcited by 97
  4. 2021Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulationcited by 82
  5. 2022Latent Plans for Task-Agnostic Offline Reinforcement Learningcited by 81
  6. 2022Learning and Retrieval from Prior Data for Skill-based Imitation Learningcited by 77
  7. 2023XSkill: Cross Embodiment Skill Discoverycited by 74
  8. 2021Accelerating Robotic Reinforcement Learning via Parameterized Action Primitivescited by 63
  9. 2022Skill-based Model-based Reinforcement Learningcited by 50
  10. 2024Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradientcited by 49
  11. 2023PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Playcited by 48
  12. 2022Dichotomy of Control: Separating What You Can Control from What You Cannotcited by 47
38 more
  1. 2021TRAIL: Near-Optimal Imitation Learning with Suboptimal Datacited by 39
  2. 2021Hierarchical Skills for Efficient Explorationcited by 37
  3. 2021SKID RAW: Skill Discovery from Raw Trajectoriescited by 22
  4. 2022Learning Options via Compressioncited by 22
  5. 2023Reparameterized Policy Learning for Multimodal Trajectory Optimizationcited by 20
  6. 2022Pretraining in Deep Reinforcement Learning: A Surveycited by 18
  7. 2022STAP: Sequencing Task-Agnostic Policiescited by 17
  8. 2024EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Datacited by 13
  9. 2024Leveraging Skills from Unlabeled Prior Data for Efficient Online Explorationcited by 13
  10. 2022Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discoverycited by 12
  11. 2021Variational Inference MPC using Tsallis Divergencecited by 11
  12. 2024Language-guided Skill Learning with Temporal Variational Inferencecited by 11
  13. 2022One After Another: Learning Incremental Skills for a Changing Worldcited by 10
  14. 2022Fairness and Bias in Robot Learningcited by 10
  15. 2024PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learningcited by 8
  16. 2025Open-World Skill Discovery from Unsegmented Demonstrationscited by 7
  17. 2022SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisitioncited by 6
  18. 2022Bayesian Nonparametrics for Offline Skill Discoverycited by 5
  19. 2024HACMan++: Spatially-Grounded Motion Primitives for Manipulationcited by 5
  20. 2023Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learningcited by 4
  21. 2023One-shot Visual Imitation via Attributed Waypoints and Demonstration Augmentationcited by 4
  22. 2023Deep Probabilistic Movement Primitives with a Bayesian Aggregatorcited by 4
  23. 2022Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysiscited by 3
  24. 2024InfoCon: Concept Discovery with Generative and Discriminative Informativenesscited by 3
  25. 2024SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Taskscited by 3
  26. 2025Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustmentcited by 3
  27. 2022DIDER: Discovering Interpretable Dynamically Evolving Relationscited by 2
  28. 2023CRISP: Curriculum Inducing Primitive Informed Subgoal Prediction for Hierarchical Reinforcement Learningcited by 2
  29. 2023Boosting Reinforcement Learning and Planning with Demonstrations: A Surveycited by 1
  30. 2023Distributed Variational Inference for Online Supervised Learningcited by 1
  31. 2023Iterative Option Discovery for Planning, by Planningcited by 1
  32. 2023Learning Multimodal Latent Dynamics for Human-Robot Interactioncited by 1
  33. 2024Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Reviewcited by 1
  34. 2025CORD: Generalizable Cooperation via Role Diversitycited by 1
  35. 2025Learning Parameterized Skills from Demonstrationscited by 1
  36. 2026Adaptive Q-Chunking for Offline-to-Online Reinforcement Learningcited by 1
  37. 2022Emergency action termination for immediate reaction in hierarchical reinforcement learningcited by 0
  38. 2023Unlock Reliable Skill Inference for Quadruped Adaptive Behavior by Skill Graphcited by 0

50 most cited of 54 All 54 (JSON)