mlsanityData to 7 Oct 2026

Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation

arXiv 2303.02646 ↗First posted 5 Mar 2023cs.ROcited by 6

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

Abstract (arXiv)

Robot control for tactile feedback-based manipulation can be difficult due to the modeling of physical contacts, partial observability of the environment, and noise in perception and control. This work focuses on solving partial observability of contact-rich manipulation tasks as a Sequence-to-Sequence (Seq2Seq)} Imitation Learning (IL) problem. The proposed Seq2Seq model produces a robot-environment interaction sequence to estimate the partially observable environment state variables. Then, the observed interaction sequence is transformed to a control sequence for the task itself. The proposed Seq2Seq IL for tactile feedback-based manipulation is experimentally validated on a door-open task in a simulated environment and a snap-on insertion task with a real robot. The model is able to learn both tasks from only 50 expert demonstrations, while state-of-the-art reinforcement learning and imitation learning methods fail.

Key facts. Cited by 6 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 27 of its 57 references that are in the map. First posted on arXiv 5 Mar 2023.

Builds on

27 of 57 references.

References · 27

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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15 more
  1. 2021Memory-based Deep Reinforcement Learning for POMDPscited by 53
  2. 2019Variational Recurrent Models for Solving Partially Observable Control Taskscited by 46
  3. 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 37
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  11. 2015A Framework for Fine Robotic Assemblycited by 7
  12. 2020Stability-Guaranteed Reinforcement Learning for Contact-rich Manipulationcited by 7
  13. 2020Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitivescited by 6
  14. 2021Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertaintycited by 6
  15. 2020Learning Sequences of Manipulation Primitives for Robotic Assemblycited by 5

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 4.

6 papers.

  1. 2024Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learningcited by 69
  2. 2024VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policiescited by 14
  3. 2025A Survey on Imitation Learning for Contact-Rich Tasks in Roboticscited by 12
  4. 2025Structured Agent Distillation for Large Language Modelcited by 11
  5. 2025Sensor-Invariant Tactile Representationcited by 8
  6. 2025Real-World Reinforcement Learning of Active Perception Behaviorscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025TLA: Tactile-Language-Action Model for Contact-Rich Manipulation
  2. 2025VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation
  3. 2020Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
  4. 2026TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation
  5. 2026Tactile Curiosity Drives Robot Interaction
  6. 2026TacCoRL: Integrating Tactile Feedback into VLA via Simulation
  7. 2026UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models
  8. 2026PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation