AnchorDP3 is a diffusion policy framework for dual-arm robotic manipulation that achieves state-of-the-art performance in highly random environments. It integrates three key innovations: (1) simulator-supervised semantic segmentation (provides robust condition priors by explicitly segmenting task-critical objects within the point cloud using rendered correct answers), (2) task-condition feature encoder (a lightweight module that processes augmented point clouds per task, enabling efficient multi-task learning via shared diffusion-based motion experts), and (3) condition-anchored key pose diffusion (dramatically simplifies the prediction space by replacing dense trajectory predictions with sparse, geometrically meaningful motion anchors, such as pre-grasp poses and grip poses anchored directly to the context); the motion experts are forced to predict robot joint angles and end-effector poses simultaneously, accelerating convergence and improving accuracy by leveraging geometric consistency. Trained on large-scale procedurally generated simulation data, AnchorDP3 achieves an average success rate of 98.7% on the RoboTwin benchmark across a wide variety of tasks under extreme randomization of objects, clutter, table height, illumination, and background. Integrated with the RoboTwin real-simulation pipeline, this framework has the potential to generate fully autonomous, deployable visual-motor policies based solely on scenes and instructions, completely eliminating human demonstration in manipulation skill learning.