High-dimensional manipulator operation in unstructured environments requires a differentiable, scene-agnostic distance query mechanism to guide safe motion generation. Existing geometric collision checkers are typically non-differentiable, while workspace-based implicit distance models are hindered by the highly nonlinear workspace--configuration mapping and often suffer from poor convergence; moreover, self-collision and environment collision are commonly handled as separate constraints. We propose Configuration-Space Signed Distance Field-Net (CSSDF-Net), which learns a continuous signed distance field directly in configuration space to provide joint-space distance and gradient queries under a unified geometric notion of safety. To enable zero-shot generalization without environment-specific retraining, we introduce a spatial-hashing-based data generation pipeline that encodes robot-centric geometric priors and supports efficient retrieval of risk configurations for arbitrary obstacle point sets. The learned distance field is integrated into safety-constrained trajectory optimization and receding-horizon MPC, enabling both offline planning and online reactive avoidance. Experiments on a planar arm and a 7-DoF manipulator demonstrate stable gradients, effective collision avoidance in static and dynamic scenes, and practical inference latency for large-scale point-cloud queries, supporting deployment in previously unseen environments.

翻译：非结构化环境中的高维机械臂操作需要一种可微分、场景无关的距离查询机制来引导安全运动生成。现有的几何碰撞检测器通常不可微分，而基于工作空间的隐式距离模型则受限于高度非线性的工作空间-构型映射，往往收敛性较差；此外，自碰撞与环境碰撞通常被作为独立约束分别处理。我们提出构型空间符号距离场网络（CSSDF-Net），该网络直接在构型空间中学习连续的符号距离场，在统一的几何安全概念下提供关节空间的距离与梯度查询。为实现无需环境特定重训练的零样本泛化，我们引入一种基于空间哈希的数据生成流水线，该流水线编码以机器人为中心的几何先验，并支持对任意障碍物点集进行高效的风险构型检索。所学习的距离场被集成到安全约束轨迹优化与滚动时域模型预测控制中，从而同时实现离线规划与在线反应式避碰。在平面臂与七自由度机械臂上的实验表明，该方法具有稳定的梯度、在静态与动态场景中有效的碰撞避免能力，以及支持大规模点云查询的实际推理延迟，从而可在未见过的环境中部署。