mlsanityData to 7 Oct 2026

Imitation Learning for High Precision Peg-in-Hole Tasks

arXiv 2101.01052 ↗First posted 26 Dec 2020cs.RO cs.LGcited by 3

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

Abstract (arXiv)

Industrial robot manipulators are not able to match the precision and speed with which humans are able to execute contact rich tasks even to this day. Therefore, as a means overcome this gap, we demonstrate generative methods for imitating a peg-in-hole insertion task in a 6-DOF robot manipulator. In particular, generative adversarial imitation learning (GAIL) is used to successfully achieve this task with a 10 um, and a 6 um peg-hole clearance on the Yaskawa GP8 industrial robot. Experimental results show that the policy successfully learns within 20 episodes from a handful of human expert demonstrations on the robot (i.e., < 10 tele-operated robot demonstrations). The insertion time improves from > 20 seconds (which also includes failed insertions) to < 15 seconds, thereby validating the effectiveness of this approach.

Key facts. Cited by 3 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 14 of its 19 references that are in the map. First posted on arXiv 26 Dec 2020.

Builds on

14 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2018One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks25 Oct

Other references · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  5. 2016End to End Learning for Self-Driving Carscited by 1,000
  6. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 342
  7. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  8. 2015Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hourscited by 208
  9. 2018One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learningcited by 146
  10. 2017Data-efficient Deep Reinforcement Learning for Dexterous Manipulationcited by 61
  11. 2017Deep Reinforcement Learning for High Precision Assembly Taskscited by 37
  12. 2019Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robotscited by 2
1 more
  1. 2019Design, Development and Experimental Realization of a Quadrupedal Research Platform: Stochcited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2024RVT-2: Learning Precise Manipulation from Few Demonstrationscited by 135
  2. 2025A Survey on Imitation Learning for Contact-Rich Tasks in Roboticscited by 12
  3. 2023Zero-Shot Transfer of Haptics-Based Object Insertion Policiescited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2021Imitation and Supervised Learning of Compliance for Robotic Assembly
  2. 2020Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks
  3. 2025Goal-based Self-Adaptive Generative Adversarial Imitation Learning (Goal-SAGAIL) for Multi-goal Robotic Manipulation Tasks
  4. 2025Continuous Control of Diverse Skills in Quadruped Robots Without Complete Expert Datasets
  5. 2022Goal-Aware Generative Adversarial Imitation Learning from Imperfect Demonstration for Robotic Cloth Manipulation
  6. 2019Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies
  7. 2022Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations
  8. 2025Zero-Shot Peg Insertion: Identifying Mating Holes and Estimating SE(2) Poses with Vision-Language Models