# SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing

arXiv: https://arxiv.org/abs/2404.02569 · HTML: https://mlsanity.com/paper/2404.02569/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Continual World: A Robotic Benchmark For Continual Reinforcement Learning](https://mlsanity.com/paper/2105.10919/) (2021)
- [Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2008.10224/) (2020)
- [RoboNinja: Learning an Adaptive Cutting Policy for Multi-Material Objects](https://mlsanity.com/paper/2302.11553/) (2023)
- [Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation](https://mlsanity.com/paper/2002.02538/) (2020)
- [RAMP: A Benchmark for Evaluating Robotic Assembly Manipulation and Planning](https://mlsanity.com/paper/2305.09644/) (2023)
- [ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction](https://mlsanity.com/paper/2210.06887/) (2022)
- [DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting](https://mlsanity.com/paper/2203.10263/) (2022)
- [Deep Segmented DMP Networks for Learning Discontinuous Motions](https://mlsanity.com/paper/2309.00320/) (2023)

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

- [Mash, Spread, Slice! Learning to Manipulate Object States via Visual Spatial Progress](https://mlsanity.com/paper/2509.24129/) (2025)
- [CulinaryCut-VLAP: A Vision-Language-Action-Physics Framework for Food Cutting via a Force-Aware Material Point Method](https://mlsanity.com/paper/2601.06451/) (2026)
- [Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning](https://mlsanity.com/paper/2604.16850/) (2026)
- [Extended Reality System for Robotic Learning from Human Demonstration](https://mlsanity.com/paper/2409.12862/) (2024)
- [Grounded Vision-Language Interpreter for Long-Horizon Bimanual Task and Motion Planning](https://mlsanity.com/paper/2506.03270/) (2025)
- [Latent Space Reinforcement Learning for Inverse Material Estimation in Food Fracture Simulation](https://mlsanity.com/paper/2606.16870/) (2026)

## Abstract (arXiv)

Cooking robots can enhance the home experience by reducing the burden of daily chores. However, these robots must perform their tasks dexterously and safely in shared human environments, especially when handling dangerous tools such as kitchen knives. This study focuses on enabling a robot to autonomously and safely learn food-cutting tasks. More specifically, our goal is to enable a collaborative robot or industrial robot arm to perform food-slicing tasks by adapting to varying material properties using compliance control. Our approach involves using Reinforcement Learning (RL) to train a robot to compliantly manipulate a knife, by reducing the contact forces exerted by the food items and by the cutting board. However, training the robot in the real world can be inefficient, and dangerous, and result in a lot of food waste. Therefore, we proposed SliceIt!, a framework for safely and efficiently learning robot food-slicing tasks in simulation. Following a real2sim2real approach, our framework consists of collecting a few real food slicing data, calibrating our dual simulation environment (a high-fidelity cutting simulator and a robotic simulator), learning compliant control policies on the calibrated simulation environment, and finally, deploying the policies on the real robot.