mlsanityData to 7 Oct 2026

Active Third-Person Imitation Learning

arXiv 2312.16365 ↗First posted 27 Dec 2023cs.LG cs.AI stat.MLcited by 1

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

Abstract (arXiv)

We consider the problem of third-person imitation learning with the additional challenge that the learner must select the perspective from which they observe the expert. In our setting, each perspective provides only limited information about the expert's behavior, and the learning agent must carefully select and combine information from different perspectives to achieve competitive performance. This setting is inspired by real-world imitation learning applications, e.g., in robotics, a robot might observe a human demonstrator via camera and receive information from different perspectives depending on the camera's position. We formalize the aforementioned active third-person imitation learning problem, theoretically analyze its characteristics, and propose a generative adversarial network-based active learning approach. Empirically, we demstrate that our proposed approach can effectively learn from expert demonstrations and explore the importance of different architectural choices for the learner's performance.

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 25 of its 48 references that are in the map. First posted on arXiv 27 Dec 2023.

Builds on

25 of 48 references.

Closest ancestors · 6

Same direction, newest first

  1. 2022Interactive Imitation Learning in Robotics: A Survey31 Oct
  2. 2020Active Imitation Learning with Noisy Guidance26 May
  3. 2019Recent Advances in Imitation Learning from Observation30 May
  4. 2019Causal Confusion in Imitation Learning28 May
  5. 2018Generative Adversarial Imitation from Observation17 Jul
  6. 2018Behavioral Cloning from Observation4 May

Other references · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  3. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  4. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  5. 2016Generative Adversarial Imitation Learningcited by 1,576
  6. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  7. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 781
  8. 2017Time-Contrastive Networks: Self-Supervised Learning from Videocited by 453
  9. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  10. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  11. 2017Third-Person Imitation Learningcited by 103
  12. 2021XIRL: Cross-embodiment Inverse Reinforcement Learningcited by 101
7 more
  1. 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
  2. 2017OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learningcited by 40
  3. 2021VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigationcited by 27
  4. 2019Interactive Teaching Algorithms for Inverse Reinforcement Learningcited by 24
  5. 2021Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learningcited by 23
  6. 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
  7. 2018Teaching Inverse Reinforcement Learners via Features and Demonstrationscited by 19

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoringcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2019Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller
  2. 2023Adversarial Imitation Learning from Visual Observations using Latent Information
  3. 2021Domain-Robust Visual Imitation Learning with Mutual Information Constraints
  4. 2020Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
  5. 2026Mirror Learning
  6. 2019A New Framework for Query Efficient Active Imitation Learning
  7. 2022Robust Learning from Observation with Model Misspecification
  8. 2019Sample-efficient Adversarial Imitation Learning from Observation