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

Haozhe Lou, Runyi Yang, Wanqi Zhong, Caiyun Liu, Yicheng Liu +8

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…