Understanding how robots perceive and interact with everyday controls requires datasets that capture both geometry and function at a fine-grained level. Codatta’s Appliance Knobs dataset on @huggingface is designed specifically for this challenge, providing high-quality, multi-view observations of appliance knobs and rotary controls that support tasks such as 3D shape understanding, pose estimation, control-state recognition, and interaction-aware perception. Built for embodied AI, robot learning, and physical intelligence research, the dataset helps models learn the subtle visual differences that correspond to meaningful functional states in real-world devices. As part of @codatta_io broader robotics data initiative, the Appliance Knobs dataset contributes to the development of next-generation robotic foundation models capable of perceiving, reasoning about, and acting within physical environments, and is available for exploration alongside Codatta’s Manipulation Trajectory datasets through Asmora: