Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional safety frameworks treat contact as unsafe, restricting robots to collision avoidance and limiting their ability to function in dense, everyday settings. As the number of objects grows, model-based approaches for safe manipulation become computationally intractable; meanwhile, learned methods typically tie safety to the task at hand, making them hard to transfer to new tasks without retraining. In this work we introduce Dense Contact Barrier Functions(DCBF). Our approach bypasses the computational complexity of explicitly modeling multi-object dynamics by instead learning a composable, object-centric function that implicitly captures the safety constraints arising from physical interactions. Trained offline on interactions with a few objects, the learned DCBFcomposes across arbitrary object sets at runtime, producing a single global safety filter that scales linearly and transfers across tasks without retraining. We validate our approach through simulated experiments in dense clutter, demonstrating its ability to enable collision-free navigation and safe, contact-rich interaction in suitable settings.

翻译：机器人在日常环境中运行时，必须在密集杂乱的场景中进行导航与操作，此时与周围物体的物理接触不可避免。传统安全框架将接触视为不安全行为，限制机器人仅能进行碰撞规避，从而制约了其在密集日常场景中的功能发挥。随着物体数量的增加，基于模型的安全操作方法在计算上变得难以处理；与此同时，学习方法通常将安全性与当前任务绑定，导致其难以迁移到新任务而无需重新训练。本研究提出密集接触屏障函数（DCBF）。该方法通过习得一种可组合的、以物体为中心的函数来规避显式建模多物体动力学的计算复杂性，该函数隐式捕获由物理交互产生的安全约束。通过与少量物体的交互进行离线训练后，习得的DCBF可在运行时跨任意物体集进行组合，生成一个线性扩展的全局安全过滤器，并能跨任务迁移而无需重新训练。我们通过在密集杂乱场景中的仿真实验验证了所提方法，证明了其在适宜场景中实现无碰撞导航及安全、高接触度交互的能力。