# Learning to Represent Haptic Feedback for Partially-Observable Tasks

arXiv: https://arxiv.org/abs/1705.06243 · HTML: https://mlsanity.com/paper/1705.06243/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

## Built on by (most cited first, 5 of 5)

- [Finite-Time Analysis of Natural Actor-Critic for POMDPs](https://mlsanity.com/paper/2202.09753/) (2022)
- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations](https://mlsanity.com/paper/2209.07682/) (2022)
- [Ensuring Force Safety in Vision-Guided Robotic Manipulation via Implicit Tactile Calibration](https://mlsanity.com/paper/2412.10349/) (2024)
- [Scalable Policy-Based RL Algorithms for POMDPs](https://mlsanity.com/paper/2510.06540/) (2025)

## Abstract (arXiv)

The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.