发表机构
University of Kaiserslautern Landau（凯泽斯劳滕-兰道大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出物理信息统一可微碰撞学习框架PI-UDF，结合正向运动学与残差网络预测臂间碰撞距离，将其集成到非线性MPC中，经双Franka平台实验验证可生成安全的协作机器人运动。
AI 中文摘要
近距离多臂操作需要兼具几何精度和足够可微性以支持实时优化的碰撞模型。经典几何检测器能提供可靠距离，但难以用于基于梯度的模型预测控制（MPC）；而保守代理模型则会限制紧密耦合的运动。本文提出PI-UDF，一种用于关节机器人间体-体碰撞距离预测的物理信息统一可微框架。PI-UDF将解析正向运动学与可学习的连杆几何嵌入、共享残差网络相结合，直接从机器人构型预测臂间成对距离。为提升安全关键场景下的保真度，本文结合配额驱动的边界挖掘与非对称越界惩罚，该惩罚强调碰撞边界附近的假安全符号误差。学习得到的距离场作为可微臂间间隙项被集成到非线性MPC中。本文在真实的双Franka平台上验证该框架，开展高速近距离14自由度（DoF）双臂交换、持续单臂动态避障，以及对移动臂采用冻结、预测和目标切换处理的动态避障规划配置实验。硬件实验与离线Drake/FCL重放结果表明，PI-UDF可提供适用于闭环碰撞感知协作机器人运动生成的可微臂间间隙估计值。
英文摘要
Close-proximity multi-arm manipulation requires collision models that are both geometrically accurate and differentiable enough for real-time optimization. Classical geometry checkers provide reliable distances but are difficult to use inside gradient-based model predictive control, while conservative proxy models can restrict tightly coupled motion. We present PI-UDF, a physics-informed unified differentiable framework for body-to-body collision distance prediction between articulated robots. PI-UDF combines analytical forward kinematics with learnable link-geometry embeddings and a shared residual network to predict pairwise inter-arm distances directly from robot configurations. To improve safety-critical fidelity, we combine quota-driven boundary mining with an asymmetric boundary-crossing penalty that emphasizes false-safe sign errors near the collision boundary. The learned distance field is integrated into nonlinear MPC as a differentiable inter-arm clearance term. We validate the framework on a real dual-Franka platform through high-speed close-proximity 14-DoF dual-arm swapping, sustained single-arm dynamic evasion, and dynamic-evasion planning configurations with frozen, predicted, and target-switching treatments of the moving arm. Hardware experiments and offline Drake/FCL replay show that PI-UDF provides a differentiable inter-arm clearance estimate suitable for closed-loop collision-aware collaborative robot motion generation.
