# Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation

arXiv: https://arxiv.org/abs/2303.02646 · HTML: https://mlsanity.com/paper/2303.02646/

## Key facts

- Cited by 6 papers in the map (data through 14 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.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model](https://mlsanity.com/paper/1907.00953/) (2019)
- [RUDDER: Return Decomposition for Delayed Rewards](https://mlsanity.com/paper/1806.07857/) (2018)
- [Deep Variational Reinforcement Learning for POMDPs](https://mlsanity.com/paper/1806.02426/) (2018)
- [Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs](https://mlsanity.com/paper/2110.05038/) (2021)
- [Primal Wasserstein Imitation Learning](https://mlsanity.com/paper/2006.04678/) (2020)
- [Memory-based Deep Reinforcement Learning for POMDPs](https://mlsanity.com/paper/2102.12344/) (2021)
- [Variational Recurrent Models for Solving Partially Observable Control Tasks](https://mlsanity.com/paper/1912.10703/) (2019)
- [Offline Meta-Reinforcement Learning for Industrial Insertion](https://mlsanity.com/paper/2110.04276/) (2021)
- [Learning Long-Term Reward Redistribution via Randomized Return Decomposition](https://mlsanity.com/paper/2111.13485/) (2021)
- [Learning Belief Representations for Imitation Learning in POMDPs](https://mlsanity.com/paper/1906.09510/) (2019)
- [Structured World Belief for Reinforcement Learning in POMDP](https://mlsanity.com/paper/2107.08577/) (2021)
- [Recurrent Off-policy Baselines for Memory-based Continuous Control](https://mlsanity.com/paper/2110.12628/) (2021)
- [A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control](https://mlsanity.com/paper/1805.08576/) (2018)
- [Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics](https://mlsanity.com/paper/1908.04696/) (2019)
- [Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability](https://mlsanity.com/paper/2010.09170/) (2020)
- [Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty](https://mlsanity.com/paper/2106.04306/) (2021)
- [A Framework for Fine Robotic Assembly](https://mlsanity.com/paper/1509.04806/) (2015)
- [Stability-Guaranteed Reinforcement Learning for Contact-rich Manipulation](https://mlsanity.com/paper/2004.10886/) (2020)
- [Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives](https://mlsanity.com/paper/2008.13223/) (2020)
- [Learning Sequences of Manipulation Primitives for Robotic Assembly](https://mlsanity.com/paper/2011.00778/) (2020)

## Built on by (most cited first, 6 of 6)

- [Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning](https://mlsanity.com/paper/2407.03162/) (2024)
- [VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies](https://mlsanity.com/paper/2403.11898/) (2024)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Structured Agent Distillation for Large Language Model](https://mlsanity.com/paper/2505.13820/) (2025)
- [Sensor-Invariant Tactile Representation](https://mlsanity.com/paper/2502.19638/) (2025)
- [Real-World Reinforcement Learning of Active Perception Behaviors](https://mlsanity.com/paper/2512.01188/) (2025)

## 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.