mlsanityData to 7 Oct 2026

HILONet: Hierarchical Imitation Learning from Non-Aligned Observations

arXiv 2011.02671 ↗First posted 5 Nov 2020cs.LGcited by 1

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

It is challenging learning from demonstrated observation-only trajectories in a non-time-aligned environment because most imitation learning methods aim to imitate experts by following the demonstration step-by-step. However, aligned demonstrations are seldom obtainable in real-world scenarios. In this work, we propose a new imitation learning approach called Hierarchical Imitation Learning from Observation(HILONet), which adopts a hierarchical structure to choose feasible sub-goals from demonstrated observations dynamically. Our method can solve all kinds of tasks by achieving these sub-goals, whether it has a single goal position or not. We also present three different ways to increase sample efficiency in the hierarchical structure. We conduct extensive experiments using several environments. The results show the improvement in both performance and learning efficiency.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 21 of its 33 references that are in the map. First posted on arXiv 5 Nov 2020.

Builds on

21 of 33 references.

Closest ancestors · 7

Same direction, newest first

  1. 2019To Follow or not to Follow: Selective Imitation Learning from Observations16 Dec
  2. 2019State Alignment-based Imitation Learning21 Nov
  3. 2019Domain Adaptive Imitation Learning30 Sep
  4. 2019Recent Advances in Imitation Learning from Observation30 May
  5. 2018Generative Adversarial Imitation from Observation17 Jul
  6. 2018Imitating Latent Policies from Observation21 May
  7. 2018Behavioral Cloning from Observation4 May

Other references · 14

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2016OpenAI Gymcited by 1,684
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2017Hindsight Experience Replaycited by 1,096
  5. 2017Time-Contrastive Networks: Self-Supervised Learning from Videocited by 453
  6. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 436
  7. 2016The Option-Critic Architecturecited by 433
  8. 2017FeUdal Networks for Hierarchical Reinforcement Learningcited by 328
  9. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 282
  10. 2017Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translationcited by 167
  11. 2017Third-Person Imitation Learningcited by 103
  12. 2017Learning human behaviors from motion capture by adversarial imitationcited by 75
2 more
  1. 2017OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learningcited by 40
  2. 2018One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Videocited by 16

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Cited by year

Citing papers per year: 2021: 1.

1 paper.

  1. 2021Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Taskscited by 13

Similar, not cited

Close in wording, no citation either way

  1. 2022Transfering Hierarchical Structure with Dual Meta Imitation Learning
  2. 2025Learning from Observation: A Survey of Recent Advances
  3. 2019Imitation Learning from Video by Leveraging Proprioception
  4. 2019Hierarchical Variational Imitation Learning of Control Programs
  5. 2025Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement
  6. 2026Towards Generalisable Imitation Learning Through Conditioned Transition Estimation and Online Behaviour Alignment
  7. 2024Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action Ranker
  8. 2023Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations