mlsanityData to 7 Oct 2026

Shared Multi-Task Imitation Learning for Indoor Self-Navigation

arXiv 1808.04503 ↗First posted 14 Aug 2018cs.CV cs.ROcited by 5

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

Abstract (arXiv)

Deep imitation learning enables robots to learn from expert demonstrations to perform tasks such as lane following or obstacle avoidance. However, in the traditional imitation learning framework, one model only learns one task, and thus it lacks of the capability to support a robot to perform various different navigation tasks with one model in indoor environments. This paper proposes a new framework, Shared Multi-headed Imitation Learning(SMIL), that allows a robot to perform multiple tasks with one model without switching among different models. We model each task as a sub-policy and design a multi-headed policy to learn the shared information among related tasks by summing up activations from all sub-policies. Compared to single or non-shared multi-headed policies, this framework is able to leverage correlated information among tasks to increase performance.We have implemented this framework using a robot based on NVIDIA TX2 and performed extensive experiments in indoor environments with different baseline solutions. The results demonstrate that SMIL has doubled the performance over nonshared multi-headed policy.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 16 of its 25 references that are in the map. First posted on arXiv 14 Aug 2018.

Builds on

16 of 25 references.

References · 16

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  2. 2014How transferable are features in deep neural networks?cited by 1,216
  3. 2016End to End Learning for Self-Driving Carscited by 1,000
  4. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 983
  5. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
  6. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  7. 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 351
  8. 2017One-Shot Imitation Learningcited by 232
  9. 2017One-Shot Visual Imitation Learning via Meta-Learningcited by 225
  10. 2016What makes ImageNet good for transfer learning?cited by 192
  11. 2016Learning to Navigate in Complex Environmentscited by 192
  12. 2017DART: Noise Injection for Robust Imitation Learningcited by 187
4 more
  1. 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
  2. 2017One Model To Learn Them Allcited by 86
  3. 2016One-shot Learning with Memory-Augmented Neural Networkscited by 60
  4. 2015Learning Multiple Tasks with Multilinear Relationship Networkscited by 11

Built on by

Cited by year

Citing papers per year: 2020: 1; 2021: 2; 2022: 2.

5 papers.

  1. 2021Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulationcited by 59
  2. 2020Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learningcited by 14
  3. 2021Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Taskscited by 13
  4. 2022Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersectionscited by 4
  5. 2022Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Divisioncited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation
  2. 2021Towards More Generalizable One-shot Visual Imitation Learning
  3. 2026Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations
  4. 2022SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning
  5. 2020Scalable Multi-Task Imitation Learning with Autonomous Improvement
  6. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
  7. 2024Instant Policy: In-Context Imitation Learning via Graph Diffusion
  8. 2025Are Learning-Based Approaches Ready for Real-World Indoor Navigation? A Case for Imitation Learning