mlsanityData to 14 Oct 2026

Interactive Imitation Learning in Robotics based on Simulations

arXiv 2209.03900 ↗First posted 26 Jul 2022cs.ROcited by 0

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

Abstract (arXiv)

The transformation towards intelligence in various industries is creating more demand for intelligent and flexible products. In the field of robotics, learning-based methods are increasingly being applied, with the purpose of training robots to learn to deal with complex and changing external environments through data. In this context, reinforcement learning and imitation learning are becoming research hotspots with their respective characteristics. However, the two have their own limitations in some cases, such as the high cost of data acquisition for reinforcement learning. Moreover, it is difficult for imitation learning to provide perfect demonstrations. As a branch of imitation learning, interactive imitation learning aims at transferring human knowledge to the agent through interactions between the demonstrator and the robot, which alleviates the difficulty of teaching. This thesis implements IIL algorithms in four simulation scenarios and conducts extensive experiments, aiming at providing exhaustive information about IIL methods both in action space and state space as well as comparison with RL methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 15 of its 32 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

15 of 32 references.

Closest ancestors · 5

Same direction, newest first

  1. 2020Fighting Copycat Agents in Behavioral Cloning from Observation Histories28 Oct
  2. 2020Interactive Imitation Learning in State-Space2 Aug
  3. 2019Causal Confusion in Imitation Learning28 May
  4. 2018HG-DAgger: Interactive Imitation Learning with Human Experts5 Oct
  5. 2018Behavioral Cloning from Observation4 May

Other references · 10

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2016OpenAI Gymcited by 1,736
  3. 2016End to End Learning for Self-Driving Carscited by 1,009
  4. 2017StarCraft II: A New Challenge for Reinforcement Learningcited by 274
  5. 2017Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translationcited by 167
  6. 2017Interactive Learning from Policy-Dependent Human Feedbackcited by 158
  7. 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
  8. 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 60
  9. 2016Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networkscited by 15
  10. 2018Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networkscited by 10

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  1. 2022Interactive Imitation Learning in Robotics: A Survey
  2. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
  3. 2021AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale
  4. 2021Imitation Learning: Progress, Taxonomies and Challenges
  5. 2024VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections
  6. 2020Human AI interaction loop training: New approach for interactive reinforcement learning
  7. 2023RLIF: Interactive Imitation Learning as Reinforcement Learning
  8. 2024A Survey of Imitation Learning Methods, Environments and Metrics