mlsanityData to 7 Oct 2026

Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study

arXiv 2103.11512 ↗First posted 21 Mar 2021cs.AI cs.ROcited by 24

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Abstract (arXiv)

Over the past several years there has been a considerable research investment into learning-based approaches to industrial assembly, but despite significant progress these techniques have yet to be adopted by industry. We argue that it is the prohibitively large design space for Deep Reinforcement Learning (DRL), rather than algorithmic limitations per se, that are truly responsible for this lack of adoption. Pushing these techniques into the industrial mainstream requires an industry-oriented paradigm which differs significantly from the academic mindset. In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark. We explain the design choices, representing several years of investigation, which enabled our DRL system to consistently outperform the integrator baseline in terms of both speed and reliability. Finally, we conclude with a competition between our DRL system and a human on a challenge task of insertion into a randomly moving target. This study suggests that DRL is capable of outperforming not only established engineered approaches, but the human motor system as well, and that there remains significant room for improvement. Videos can be found on our project website: https://sites.google.com/view/shield-nist.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 21 Mar 2021.

Builds on

18 of 26 references.

References · 18

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  3. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  4. 2017Hindsight Experience Replaycited by 1,096
  5. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 685
  6. 2020AWAC: Accelerating Online Reinforcement Learning with Offline Datasetscited by 647
  7. 2017Overcoming Exploration in Reinforcement Learning with Demonstrationscited by 322
  8. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 302
  9. 2019Challenges of Real-World Reinforcement Learningcited by 282
  10. 2018Distributed Prioritized Experience Replaycited by 260
  11. 2018Residual Reinforcement Learning for Robot Controlcited by 242
  12. 2018Reinforcement and Imitation Learning for Diverse Visuomotor Skillscited by 124
6 more
  1. 2020COG: Connecting New Skills to Past Experience with Offline Reinforcement Learningcited by 83
  2. 2020DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correctioncited by 71
  3. 2017Deep Reinforcement Learning for High Precision Assembly Taskscited by 37
  4. 2018A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learningcited by 35
  5. 2019Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assemblycited by 33
  6. 2020Meta-Reinforcement Learning for Robotic Industrial Insertion Taskscited by 28

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 5; 2023: 4; 2024: 7; 2025: 2; 2026: 2.

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  1. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  2. 2024SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learningcited by 113
  3. 2023RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitationcited by 88
  4. 2023Multi-Stage Cable Routing through Hierarchical Imitation Learningcited by 59
  5. 2021CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulationcited by 49
  6. 2022Factory: Fast Contact for Robotic Assemblycited by 49
  7. 2022Vision-Based Manipulators Need to Also See from Their Handscited by 46
  8. 2024RLDG: Robotic Generalist Policy Distillation via Reinforcement Learningcited by 42
  9. 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 37
  10. 2023RLIF: Interactive Imitation Learning as Reinforcement Learningcited by 24
  11. 2024IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learningcited by 24
  12. 2021Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulationcited by 13
12 more
  1. 2025A Survey on Imitation Learning for Contact-Rich Tasks in Roboticscited by 12
  2. 2022Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Visioncited by 11
  3. 2024MILES: Making Imitation Learning Easy with Self-Supervisioncited by 9
  4. 2021Multi-Task Learning with Sequence-Conditioned Transporter Networkscited by 8
  5. 2022Reinforcement Learning in Practice: Opportunities and Challengescited by 5
  6. 2024Fast Ergodic Search with Kernel Functionscited by 5
  7. 2025Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Surveycited by 3
  8. 2023Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environmentscited by 2
  9. 2022Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holescited by 0
  10. 2024COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertaintycited by 0
  11. 2026Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertaintycited by 0
  12. 2026Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning frameworkcited by 0