Search Results for author:
Found 8 papers, 5 papers with code
Towards Equitable Agile Research and Development of AI and Robotics
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.
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation
Accurate representation in media is known to improve the well-being of the people who consume it.
Robots Enact Malignant Stereotypes
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].
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer
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.
Guiding Multi-Step Rearrangement Tasks with Natural Language Instructions
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.
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer
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.
sharpDARTS: Faster and More Accurate Differentiable Architecture Search
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).
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
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.
