Evaluation of: image feature quantization effects on object grasp pose estimation in a tabletop manipulation environment; of high-level planner accuracy on a prepared set of simple command permutations

A small data set that was used to test the effects of quantizing pixel-wise embedding vectors in an open-set image segmentation and spatial mapping system. Contains:rgb images of the scenesdepth images of the scenesground truth reference positions and grasp poses generated semi-automatically by human labelersinferred object grasp poses using two different mapping systems, a 2.5d approach processing the RGB-D images from a Zivid camera directly, and a 3d approach integrating multiple RGB-D images from a realsense camera into an octree occupancy map data structureAddition: a supplementary data set including the outputs from evaluating an LLM-based high-level planner on a set of natural language queries has been appended.