mlsanityData to 7 Oct 2026

Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations

arXiv 2206.04632 ↗First posted 9 Jun 2022cs.RO cs.AI cs.FLcited by 10

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

Abstract (arXiv)

Learning from demonstration (LfD) has succeeded in tasks featuring a long time horizon. However, when the problem complexity also includes human-in-the-loop perturbations, state-of-the-art approaches do not guarantee the successful reproduction of a task. In this work, we identify the roots of this challenge as the failure of a learned continuous policy to satisfy the discrete plan implicit in the demonstration. By utilizing modes (rather than subgoals) as the discrete abstraction and motion policies with both mode invariance and goal reachability properties, we prove our learned continuous policy can simulate any discrete plan specified by a linear temporal logic (LTL) formula. Consequently, an imitator is robust to both task- and motion-level perturbations and guaranteed to achieve task success. Project page: https://yanweiw.github.io/tli/

Key facts. Cited by 10 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 16 of its 59 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

16 of 59 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Elaborating on Learned Demonstrations with Temporal Logic Specifications3 Feb

Other references · 15

  1. 2021Convex optimizationcited by 2,479
  2. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 685
  3. 2018An Algorithmic Perspective on Imitation Learningcited by 455
  4. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learningcited by 380
  5. 2020Integrated Task and Motion Planningcited by 281
  6. 2022Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methodscited by 148
  7. 2020Learning to Generalize Across Long-Horizon Tasks from Human Demonstrationscited by 114
  8. 2021Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functionscited by 91
  9. 2020Learning Control Barrier Functions from Expert Demonstrationscited by 88
  10. 2017Multi-Level Discovery of Deep Optionscited by 50
  11. 2021Dynamic Movement Primitives in Robotics: A Tutorial Surveycited by 45
  12. 2020Learning Barrier Functions for Constrained Motion Planning with Dynamical Systemscited by 17
3 more
  1. 2020Modeling Long-horizon Tasks as Sequential Interaction Landscapescited by 14
  2. 2017Interpretable Apprenticeship Learning with Temporal Logic Specificationscited by 13
  3. 2021Reactive Task and Motion Planning under Temporal Logic Specificationscited by 9

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 6; 2025: 1.

10 papers.

  1. 2022Learning Neuro-Symbolic Skills for Bilevel Planningcited by 55
  2. 2024Inference-Time Policy Steering through Human Interactionscited by 35
  3. 2024Grounding Language Plans in Demonstrations Through Counterfactual Perturbationscited by 11
  4. 2023Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policiescited by 10
  5. 2023Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensorcited by 9
  6. 2024RecoveryChaining: Learning Local Recovery Policies for Robust Manipulationcited by 8
  7. 2024Diffusion Meets Options: Hierarchical Generative Skill Composition for Temporally-Extended Taskscited by 7
  8. 2024Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overviewcited by 3
  9. 2024Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environmentscited by 1
  10. 2025Structured Imitation Learning of Interactive Policies through Inverse Gamescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Learning from Demonstrations using Signal Temporal Logic
  2. 2018Automata Guided Reinforcement Learning With Demonstrations
  3. 2026Zero-Shot Instruction Following in RL via Structured LTL Representations
  4. 2025Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning
  5. 2022Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning
  6. 2019Motion Reasoning for Goal-Based Imitation Learning
  7. 2023GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction Estimation
  8. 2023Hierarchical Imitation Learning with Vector Quantized Models