no code implementations • 8 Jul 2024 • Boyang Wang, Nikhil Sridhar, Chao Feng, Mark Van der Merwe, Adam Fishman, Nima Fazeli, Jeong Joon Park
We propose a robot learning method for communicating, planning, and executing a wide range of tasks, dubbed This&That.
no code implementations • 23 May 2023 • Mark Van der Merwe, Youngsun Wi, Dmitry Berenson, Nima Fazeli
Representing the object geometry and contact with the environment implicitly allows a single model to predict contact patches of varying complexity.
1 code implementation • 30 Sep 2022 • Yizhou Chen, Andrea Sipos, Mark Van der Merwe, Nima Fazeli
Learning representations in the joint domain of vision and touch can improve manipulation dexterity, robustness, and sample-complexity by exploiting mutual information and complementary cues.
no code implementations • 17 Mar 2021 • Shreyansh Daftry, Barry Ridge, William Seto, Tu-Hoa Pham, Peter Ilhardt, Gerard Maggiolino, Mark Van der Merwe, Alex Brinkman, John Mayo, Eric Kulczyski, Renaud Detry
A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA.
no code implementations • 5 Mar 2021 • Tu-Hoa Pham, William Seto, Shreyansh Daftry, Barry Ridge, Johanna Hansen, Tristan Thrush, Mark Van der Merwe, Gerard Maggiolino, Alexander Brinkman, John Mayo, Yang Cheng, Curtis Padgett, Eric Kulczycki, Renaud Detry
This work informs the Mars Sample Return campaign on the choice of a site where Perseverance (R0) will place a set of sample tubes for future retrieval by another rover (R1).
no code implementations • 6 Jun 2020 • Qingkai Lu, Mark Van der Merwe, Tucker Hermans
We show that our active grasp learning approach uses fewer training samples to produce grasp success rates comparable with the passive supervised learning method trained with grasping data generated by an analytical planner.
Robotics
no code implementations • 25 Jan 2020 • Qingkai Lu, Mark Van der Merwe, Balakumar Sundaralingam, Tucker Hermans
We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success.
Robotics
no code implementations • 2 Oct 2019 • Mark Van der Merwe, Qingkai Lu, Balakumar Sundaralingam, Martin Matak, Tucker Hermans
We leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization.
1 code implementation • 24 Sep 2019 • Mark Van der Merwe, Vinu Joseph, Ganesh Gopalakrishnan
Belief Propagation (BP) is a message-passing algorithm for approximate inference over Probabilistic Graphical Models (PGMs), finding many applications such as computer vision, error-correcting codes, and protein-folding.