# RoboNinja: Learning an Adaptive Cutting Policy for Multi-Material Objects

arXiv: https://arxiv.org/abs/2302.11553 · HTML: https://mlsanity.com/paper/2302.11553/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2023.

## Built on by (most cited first, 21 of 21)

- [RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation](https://mlsanity.com/paper/2311.01455/) (2023)
- [FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation](https://mlsanity.com/paper/2303.02346/) (2023)
- [Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation](https://mlsanity.com/paper/2303.11057/) (2023)
- [A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation](https://mlsanity.com/paper/2602.01067/) (2026)
- [Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies](https://mlsanity.com/paper/2411.00781/) (2024)
- [ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation](https://mlsanity.com/paper/2503.03045/) (2025)
- [Towards Generalist Robots: A Promising Paradigm via Generative Simulation](https://mlsanity.com/paper/2305.10455/) (2023)
- [Learning Manipulation Skills through Robot Chain-of-Thought with Sparse Failure Guidance](https://mlsanity.com/paper/2405.13573/) (2024)
- [DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment](https://mlsanity.com/paper/2410.11584/) (2024)
- [Learning Generalizable Tool-use Skills through Trajectory Generation](https://mlsanity.com/paper/2310.00156/) (2023)
- [DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics](https://mlsanity.com/paper/2312.06408/) (2023)
- [SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing](https://mlsanity.com/paper/2404.02569/) (2024)
- [One-shot Video Imitation via Parameterized Symbolic Abstraction Graphs](https://mlsanity.com/paper/2408.12674/) (2024)
- [Real-Time Verification of Embodied Reasoning for Generative Skill Acquisition](https://mlsanity.com/paper/2505.11175/) (2025)
- [TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies](https://mlsanity.com/paper/2509.19712/) (2025)
- [Mash, Spread, Slice! Learning to Manipulate Object States via Visual Spatial Progress](https://mlsanity.com/paper/2509.24129/) (2025)
- [How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference](https://mlsanity.com/paper/2603.03280/) (2026)
- [GRITS: A Spillage-Aware Guided Diffusion Policy for Robot Food Scooping Tasks](https://mlsanity.com/paper/2510.00573/) (2025)
- [Latent Space Reinforcement Learning for Inverse Material Estimation in Food Fracture Simulation](https://mlsanity.com/paper/2606.16870/) (2026)
- [GROW2: Grounding Which and Where for Robot Tool Use](https://mlsanity.com/paper/2606.30632/) (2026)
- [DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine](https://mlsanity.com/paper/2610.17208/) (2026)

## Abstract (arXiv)

We introduce RoboNinja, a learning-based cutting system for multi-material objects (i.e., soft objects with rigid cores such as avocados or mangos). In contrast to prior works using open-loop cutting actions to cut through single-material objects (e.g., slicing a cucumber), RoboNinja aims to remove the soft part of an object while preserving the rigid core, thereby maximizing the yield. To achieve this, our system closes the perception-action loop by utilizing an interactive state estimator and an adaptive cutting policy. The system first employs sparse collision information to iteratively estimate the position and geometry of an object's core and then generates closed-loop cutting actions based on the estimated state and a tolerance value. The "adaptiveness" of the policy is achieved through the tolerance value, which modulates the policy's conservativeness when encountering collisions, maintaining an adaptive safety distance from the estimated core. Learning such cutting skills directly on a real-world robot is challenging. Yet, existing simulators are limited in simulating multi-material objects or computing the energy consumption during the cutting process. To address this issue, we develop a differentiable cutting simulator that supports multi-material coupling and allows for the generation of optimized trajectories as demonstrations for policy learning. Furthermore, by using a low-cost force sensor to capture collision feedback, we were able to successfully deploy the learned model in real-world scenarios, including objects with diverse core geometries and soft materials.