# Learning to Navigate Cloth using Haptics

arXiv: https://arxiv.org/abs/1703.06905 · HTML: https://mlsanity.com/paper/1703.06905/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments](https://mlsanity.com/paper/2206.12493/) (2022)
- [Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery](https://mlsanity.com/paper/2311.14270/) (2023)
- [Unveiling Options with Neural Decomposition](https://mlsanity.com/paper/2410.11262/) (2024)

## Abstract (arXiv)

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.