# Learning Robot Skills with Temporal Variational Inference

arXiv: https://arxiv.org/abs/2006.16232 · HTML: https://mlsanity.com/paper/2006.16232/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 30 of 54)

- [RT-H: Action Hierarchies Using Language](https://mlsanity.com/paper/2403.01823/) (2024)
- [OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning](https://mlsanity.com/paper/2010.13611/) (2020)
- [Reinforcement Learning with Action Chunking](https://mlsanity.com/paper/2507.07969/) (2025)
- [Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation](https://mlsanity.com/paper/2109.13841/) (2021)
- [Latent Plans for Task-Agnostic Offline Reinforcement Learning](https://mlsanity.com/paper/2209.08959/) (2022)
- [Learning and Retrieval from Prior Data for Skill-based Imitation Learning](https://mlsanity.com/paper/2210.11435/) (2022)
- [XSkill: Cross Embodiment Skill Discovery](https://mlsanity.com/paper/2307.09955/) (2023)
- [Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives](https://mlsanity.com/paper/2110.15360/) (2021)
- [Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient](https://mlsanity.com/paper/2406.00681/) (2024)
- [Skill-based Model-based Reinforcement Learning](https://mlsanity.com/paper/2207.07560/) (2022)
- [PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play](https://mlsanity.com/paper/2312.04549/) (2023)
- [Dichotomy of Control: Separating What You Can Control from What You Cannot](https://mlsanity.com/paper/2210.13435/) (2022)
- [TRAIL: Near-Optimal Imitation Learning with Suboptimal Data](https://mlsanity.com/paper/2110.14770/) (2021)
- [Hierarchical Skills for Efficient Exploration](https://mlsanity.com/paper/2110.10809/) (2021)
- [SKID RAW: Skill Discovery from Raw Trajectories](https://mlsanity.com/paper/2103.14610/) (2021)
- [Learning Options via Compression](https://mlsanity.com/paper/2212.04590/) (2022)
- [Reparameterized Policy Learning for Multimodal Trajectory Optimization](https://mlsanity.com/paper/2307.10710/) (2023)
- [STAP: Sequencing Task-Agnostic Policies](https://mlsanity.com/paper/2210.12250/) (2022)
- [Pretraining in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2211.03959/) (2022)
- [EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data](https://mlsanity.com/paper/2406.17768/) (2024)
- [Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration](https://mlsanity.com/paper/2410.18076/) (2024)
- [Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery](https://mlsanity.com/paper/2212.01105/) (2022)
- [Variational Inference MPC using Tsallis Divergence](https://mlsanity.com/paper/2104.00241/) (2021)
- [Language-guided Skill Learning with Temporal Variational Inference](https://mlsanity.com/paper/2402.16354/) (2024)
- [One After Another: Learning Incremental Skills for a Changing World](https://mlsanity.com/paper/2203.11176/) (2022)
- [Fairness and Bias in Robot Learning](https://mlsanity.com/paper/2207.03444/) (2022)
- [PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning](https://mlsanity.com/paper/2403.00929/) (2024)
- [Open-World Skill Discovery from Unsegmented Demonstrations](https://mlsanity.com/paper/2503.10684/) (2025)
- [SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition](https://mlsanity.com/paper/2202.04849/) (2022)
- [HACMan++: Spatially-Grounded Motion Primitives for Manipulation](https://mlsanity.com/paper/2407.08585/) (2024)

## 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.