no code implementations • 13 Feb 2024 • Andrew Hundt, Julia Schuller, Severin Kacianka
We propose a framework for adapting widely practiced Research and Development (R&D) project management methodologies to build organizational equity capabilities and better integrate known evidence-based best practices.
no code implementations • 16 Jan 2024 • Zhixuan Liu, Peter Schaldenbrand, Beverley-Claire Okogwu, Wenxuan Peng, Youngsik Yun, Andrew Hundt, Jihie Kim, Jean Oh
Accurate representation in media is known to improve the well-being of the people who consume it.
no code implementations • 23 Jul 2022 • Andrew Hundt, William Agnew, Vicky Zeng, Severin Kacianka, Matthew Gombolay
Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14].
1 code implementation • Conference On Robot Learning (CoRL) 2021 • Andrew Hundt, Aditya Murali, Priyanka Hubli, Ran Liu, Nakul Gopalan, Matthew Gombolay, Gregory D. Hager
Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-specific training.
2 code implementations • Conference On Robot Learning (CoRL) 2021 • Elias Stengel-Eskin, Andrew Hundt, Zhuohong He, Aditya Murali, Nakul Gopalan, Matthew Gombolay, Gregory Hager
Our model completes block manipulation tasks with synthetic commands 530 more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning.
1 code implementation • 25 Sep 2019 • Andrew Hundt, Benjamin Killeen, Nicholas Greene, Hongtao Wu, Heeyeon Kwon, Chris Paxton, Gregory D. Hager
We are able to create real stacks in 100% of trials with 61% efficiency and real rows in 100% of trials with 59% efficiency by directly loading the simulation-trained model on the real robot with no additional real-world fine-tuning.
2 code implementations • 23 Mar 2019 • Andrew Hundt, Varun Jain, Gregory D. Hager
We have performed an in-depth analysis to identify limitations in a widely used search space and a recent architecture search method, Differentiable Architecture Search (DARTS).
3 code implementations • 27 Oct 2018 • Andrew Hundt, Varun Jain, Chia-Hung Lin, Chris Paxton, Gregory D. Hager
We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances.