Path planning for a robotic system in high-dimensional cluttered environments needs to be efficient, safe, and adaptable for different environments and hardware. Conventional methods face high computation time and require extensive parameter tuning, while prior learning-based methods still fail to generalize effectively. The primary goal of this research is to develop a path planning framework capable of generalizing to unseen environments and new robotic manipulators without the need for retraining. We present GADGET (Generalizable and Adaptive Diffusion-Guided Environment-aware Trajectory generation), a diffusion-based planning model that generates joint-space trajectories conditioned on voxelized scene representations as well as start and goal configurations. A key innovation is GADGET's hybrid dual-conditioning mechanism that combines classifier-free guidance via learned scene encoding with classifier-guided Control Barrier Function (CBF) safety shaping, integrating environment awareness with real-time collision avoidance directly in the denoising process. This design supports zero-shot transfer to new environments and robotic embodiments without retraining. Experimental results show that GADGET achieves high success rates with low collision intensity in spherical-obstacle, bin-picking, and shelf environments, with CBF guidance further improving safety. Moreover, comparative evaluations indicate strong performance relative to both sampling-based and learning-based baselines. Furthermore, GADGET provides transferability across Franka Panda, Kinova Gen3 (6/7-DoF), and UR5 robots, and physical execution on a Kinova Gen3 demonstrates its ability to generate safe, collision-free trajectories in real-world settings.

翻译：高维复杂环境下机器人系统的路径规划需要高效、安全且能适应不同环境与硬件。传统方法面临计算时间长、需大量参数调优的问题，而现有基于学习的方法仍难以实现有效泛化。本研究的主要目标是开发一种能够泛化至未见环境及新型机械臂且无需重新训练的路径规划框架。我们提出GADGET（可泛化自适应扩散引导环境感知轨迹生成模型），这是一种基于扩散的规划模型，其以体素化场景表征及起始-目标构型为条件生成关节空间轨迹。核心创新在于GADGET采用混合双条件机制：通过学习型场景编码实现无分类器引导，同时结合基于控制屏障函数（CBF）安全塑形的分类器引导，在去噪过程中直接融合环境感知与实时避障能力。该设计支持零样本迁移至新环境与新机器人构型而无需重新训练。实验结果表明，GADGET在球形障碍物场景、箱内取物场景及货架场景中均能以低碰撞强度实现高成功率，CBF引导进一步提升了安全性。对比评估显示，其相对于基于采样与基于学习的基线方法均表现出优越性能。此外，GADGET在Franka Panda、Kinova Gen3（6/7自由度）及UR5机器人间展现出跨平台可迁移性，在Kinova Gen3上的物理执行实验验证了其在真实场景中生成安全无碰撞轨迹的能力。