# Leveraging Multimodal Haptic Sensory Data for Robust Cutting

arXiv: https://arxiv.org/abs/1909.12460 · HTML: https://mlsanity.com/paper/1909.12460/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [Twisting Lids Off with Two Hands](https://mlsanity.com/paper/2403.02338/) (2024)
- [Stabilize to Act: Learning to Coordinate for Bimanual Manipulation](https://mlsanity.com/paper/2309.01087/) (2023)
- [Learning Bimanual Scooping Policies for Food Acquisition](https://mlsanity.com/paper/2211.14652/) (2022)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [Hearing Touch: Audio-Visual Pretraining for Contact-Rich Manipulation](https://mlsanity.com/paper/2405.08576/) (2024)
- [PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-rich Manipulation Using Tactile-Diffusion Policies](https://mlsanity.com/paper/2504.19341/) (2025)
- [FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishes](https://mlsanity.com/paper/2407.07561/) (2024)
- [FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization](https://mlsanity.com/paper/2506.14968/) (2025)
- [Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations](https://mlsanity.com/paper/2209.07682/) (2022)
- [Learning Sequential Acquisition Policies for Robot-Assisted Feeding](https://mlsanity.com/paper/2309.05197/) (2023)
- [Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions](https://mlsanity.com/paper/2412.13157/) (2024)
- [MResT: Multi-Resolution Sensing for Real-Time Control with Vision-Language Models](https://mlsanity.com/paper/2401.14502/) (2024)
- [REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding](https://mlsanity.com/paper/2410.10017/) (2024)
- [A Framework for Multisensory Foresight for Embodied Agents](https://mlsanity.com/paper/2109.07561/) (2021)
- [GRITS: A Spillage-Aware Guided Diffusion Policy for Robot Food Scooping Tasks](https://mlsanity.com/paper/2510.00573/) (2025)

## Abstract (arXiv)

Cutting is a common form of manipulation when working with divisible objects such as food, rope, or clay. Cooking in particular relies heavily on cutting to divide food items into desired shapes. However, cutting food is a challenging task due to the wide range of material properties exhibited by food items. Due to this variability, the same cutting motions cannot be used for all food items. Sensations from contact events, e.g., when placing the knife on the food item, will also vary depending on the material properties, and the robot will need to adapt accordingly. In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events. The robot learns neural networks for performing each of these tasks and generalizing across different material properties. By adapting and monitoring the skill executions, the robot is able to reliably cut through more than 20 different types of food items and even detect whether certain food items are fresh or old.