News/Research

Ken Goldberg and Team on Orienting Novel 3D Objects

15 Jun, 2021

Ken Goldberg and Team on Orienting Novel 3D Objects

BCNM faculty Ken Goldberg and his team recently published a paper titled "Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms."

From the abstract:

Orienting objects is a critical component in the automation of many packing and assembly tasks. We present an algorithm to orient novel objects given a depth image of the object in its current and desired orientation. We formulate a self-supervised objective for this problem and train a deep neural network to estimate the 3D rotation as parameterized by a quaternion, between these current and desired depth images. We then use the trained network in a proportional controller to re-orient objects based on the estimated rotation between the two depth images. Results suggest that in simulation we can rotate unseen objects with unknown geometries by up to 30 with a median angle error of 1.47 over 100 random initial/desired orientations each for 22 novel objects. Experiments on physical objects suggest that the controller can achieve a median angle error of 4.2 over 10 random initial/desired orientations each for 5 objects.

Read the entire paper here!