Automating chicken shoulder deboning requires precise 6-DoF cutting through a partially occluded, deformable, multi-material joint, since contact with the bones presents serious health and safety risks. Our work makes both systems-level and algorithmic contributions to train and deploy a reactive force-feedback cutting policy that dynamically adapts a nominal trajectory and enables full 6-DoF knife control to traverse the narrow joint gap while avoiding contact with the bones. First, we introduce an open-source custom-built simulator for multi-material cutting that models coupling, fracture, and cutting forces, and supports reinforcement learning, enabling efficient training and rapid prototyping. Second, we design a reusable physical testbed to emulate the chicken shoulder: two rigid "bone" spheres with controllable pose embedded in a softer block, enabling rigorous and repeatable evaluation while preserving essential multi-material characteristics of the target problem. Third, we train and deploy a residual RL policy, with discretized force observations and domain randomization, enabling robust zero-shot sim-to-real transfer and the first demonstration of a learned policy that debones a real chicken shoulder. Our experiments in our simulator, on our physical testbed, and on real chicken shoulders show that our learned policy reliably navigates the joint gap and reduces undesired bone/cartilage contact, resulting in up to a 4x improvement over existing open-loop cutting baselines in terms of success rate and bone avoidance. Our results also illustrate the necessity of force feedback for safe and effective multi-material cutting. The project website is at https://hal-zhaodong-yang.github.io/MultiMaterialWebsite/.

翻译：自动化鸡肩去骨需要对部分遮挡、可变形、多材料关节进行精确的六自由度切割，因为与骨骼接触会带来严重的健康和安全风险。我们的工作在系统层面和算法层面均做出贡献，以训练和部署一种响应式力反馈切割策略，该策略能动态调整标称轨迹，实现完整的六自由度刀具控制，从而在避免接触骨骼的同时穿越狭窄的关节间隙。首先，我们引入了一个开源的自建多材料切割模拟器，该模拟器能模拟耦合、断裂和切割力，并支持强化学习，从而实现高效训练和快速原型设计。其次，我们设计了一个可重复使用的物理测试平台来模拟鸡肩：将两个具有可控姿态的刚性“骨骼”球体嵌入较软的块体中，使得在保留目标问题关键多材料特性的同时，能够进行严格且可重复的评估。第三，我们训练并部署了一个残差强化学习策略，该策略采用离散化的力观测和领域随机化，实现了鲁棒的零样本从仿真到现实的迁移，并首次展示了能对真实鸡肩进行去骨的学习策略。我们在模拟器、物理测试平台以及真实鸡肩上的实验表明，学习策略能可靠地穿越关节间隙，并减少不必要的骨骼/软骨接触，与现有的开环切割基线相比，在成功率和骨骼规避方面实现了高达4倍的提升。我们的结果也阐明了力反馈对安全有效的多材料切割的必要性。项目网站为 https://hal-zhaodong-yang.github.io/MultiMaterialWebsite/。