# DexNinja：用 Real-to-Sim-to-Real 数据引擎学习鲁棒灵巧切割策略

> 原文链接：https://robotics.hellobike.com/papers/2026-10-12-2610-17208
> 频道：papers | 更新日期：2026-10-12 | 标签：arxiv, cs.RO
> 核心摘要：DexNinja针对灵巧切割数据昂贵与仿真鸿沟，用real2sim2real生成训练分布，并在食物切片任务上验证。

# DexNinja：用 Real-to-Sim-to-Real 数据引擎学习鲁棒灵巧切割策略

::: v-pre
> 原文链接：[DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine](https://arxiv.org/abs/2610.17208)（arXiv 2610.17208 · 发布日期：2026-10-12）

**DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine**

DexNinja针对灵巧切割数据昂贵与仿真鸿沟，用real2sim2real生成训练分布，并在食物切片任务上验证。

## 核心要点

- 灵巧切割涉及高接触、可变形和拓扑变化物体，真实采集昂贵且易污染或损坏食物。
- 方法从少量真实演示重建物体实例，按类别约束随机化物理参数，生成增强操作片段。
- 自定义可微仿真器耦合机器人动力学、触觉接触与可变形切割，支持数据增强。
- 在食物切片任务中，数十个真实演示加数百仿真数据成功率超过60%，并提升超20%。

**验证范围与局限**：验证范围为灵巧食物切片任务，包含真实演示与仿真增强数据，并测试不同形状和尺寸的分布外物体。

## 英文摘要 (Abstract)

Cutting a piece of food looks effortless in human hands. For a robot, however, every cut is a high-contact interaction with a deformable, elasto-plastic object whose behavior changes with pose, contact mode, and cutting stage. Collecting real-world data is expensive and wasteful: each trial typically contaminates or damages the food, so obtaining scalable real world data for dexterous cutting is expensive and difficult. Simulation is an appealing alternative, but a large sim-to-real gap exists in both physics and perception. This gap is especially severe for topology-changing interactions like cutting, where small errors in contact, friction, or material and elasticity responses quickly compound. We propose \textsc{DexNinja}, a real2sim2real framework that turns a handful of real demonstrations into a dense and realistic training distribution. \textsc{DexNinja} (i) reconstructs object instances from real captures, (ii) randomizes physically meaningful parameters under category-level constraints, and (iii) produces augmented dexterous manipulation episodes using a custom differentiable simulator that couples robot dynamics, tactile contact, and deformable cutting. We evaluate \textsc{DexNinja} on a dexterous food-slicing task and show that (1) with tens of real demonstrations and hundreds of augmented simulator data, the success rate could reach more than 60\%. (2) the augmented data consistently improves success rates when only using real data with more than 20\% via sim-to-real, and (3) the resulting policies generalize to out-of-distribution objects with varying shapes and sizes.

作者：Haozhe Lou, Runyi Yang, Wanqi Zhong, Caiyun Liu, Yicheng Liu, Wenhao Liu, Wenlong Ma, Jiawei Xia, Kanzhi Wu, Danda Pani Paudel
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## 原始参考来源 (Sources)

- [arXiv 2610.17208：DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine](https://arxiv.org/abs/2610.17208)