We’re excited to have three lab papers that will be presented at this year’s (virtual) ICRA 2020 conference! Highlights and video links are below.

Check out our new extension to DPC-Net (from ICRA 2018): we show that DPC networks can be trained in a fully self-supervised manner, which improves accuracy and allows for retraining online in new environments!

Got features? Our recent RA-L and ICRA 2020 work demonstrates how to learn maximally-matchable image mappings to dramatically reduce the data needed for experience-based navigation.

Check out our work on a QCQP approach to inverse kinematics for redundant manipulators. We show that this difficult, nonconvex problem often admits a provably tight convex relaxation that can be efficiently solved! Coming soon to MoveIt!