MIT CSAIL Refines Picker Robots’ Ability To Handle New Objects
Picker robots — that is, motorized pincers which pick up and place things — might have repeatability in their favor, but complex poses and unfamiliar objects pose a challenge for most of them. It’s no wonder why: They not only have to locate objects and understand how to grasp them, which requires an enormous amount of training data, but they’ve got to set them down such that they don’t sustain damage or disturb their surroundings.
Leave it to the folks at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), though, to pioneer an approach that overcomes those barriers. In a newly published research paper (“Category-Level Robotic Manipulation with K-PAM: Key-Point Affordance Manipulation“), they describe a system — Keypoint Affordance Manipulation, or “kPAM” for short — that detects a collection of target coordinates called keypoints, enabling robotic hardware on which it’s deployed to handle a range of objects with finesse. READ MORE ON: VENTURE BEAT