  While natural language offers a convenient shared interface for humans and
robots, enabling robots to interpret and follow language commands remains a
longstanding challenge in manipulation. A crucial step to realizing a
performant instruction-following robot is achieving semantic manipulation,
where a robot interprets language at different specificities, from high-level
instructions like "Pick up the stuffed animal" to more detailed inputs like
"Grab the left ear of the elephant." To tackle this, we propose Keypoints +
Instructions to Execution (KITE), a two-step framework for semantic
manipulation which attends to both scene semantics (distinguishing between
different objects in a visual scene) and object semantics (precisely localizing
different parts within an object instance). KITE first grounds an input
instruction in a visual scene through 2D image keypoints, providing a highly
accurate object-centric bias for downstream action inference. Provided an RGB-D
scene observation, KITE then executes a learned keypoint-conditioned skill to
carry out the instruction. The combined precision of keypoints and
parameterized skills enables fine-grained manipulation with generalization to
scene and object variations. Empirically, we demonstrate KITE in 3 real-world
environments: long-horizon 6-DoF tabletop manipulation, semantic grasping, and
a high-precision coffee-making task. In these settings, KITE achieves a 75%,
70%, and 71% overall success rate for instruction-following, respectively. KITE
outperforms frameworks that opt for pre-trained visual language models over
keypoint-based grounding, or omit skills in favor of end-to-end visuomotor
control, all while being trained from fewer or comparable amounts of
demonstrations. Supplementary material, datasets, code, and videos can be found
on our website: http://tinyurl.com/kite-site.

Robotics;Computer Vision and Pattern Recognition