10 citations · 13 across the 10 of their papers we have counts for
13 papers
Demonstrating Multi-Suction Item Picking at Scale via Multi-Modal Learning of Pick Success
Che Wang, Jeroen van Baar, Chaitanya Mitash +6
This work demonstrates how autonomously learning aspects of robotic operation from sparsely-labeled, real-world data of deployed, engineered solutions at industrial scale can provi…
Lightweight Uncertainty Quantification with Simplex Semantic Segmentation for Terrain Traversability
Judith Dijk, Gertjan Burghouts, Kapil D. Katyal +5
For navigation of robots, image segmentation is an important component to determining a terrain's traversability. For safe and efficient navigation, it is key to assess the uncerta…
GeoDiffuser: Geometry-Based Image Editing with Diffusion Models
Rahul Sajnani, Jeroen Vanbaar, Jie Min +2
The success of image generative models has enabled us to build methods that can edit images based on text or other user input. However, these methods are bespoke, imprecise, requir…
Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances
Arun V. Reddy, Ketul Shah, William Paul +6
Human action recognition is a challenging problem, particularly when there is high variability in factors such as subject appearance, backgrounds and viewpoint. While deep neural n…
Meta Arcade: A Configurable Environment Suite for Meta-Learning
Edward W. Staley, Chace Ashcraft, Benjamin Stoler +4
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time. As a result, most existing research benchmarks consist of individual games or suites…
Complex Terrain Navigation via Model Error Prediction
Adam Polevoy, Craig Knuth, Katie M. Popek +1
Robot navigation traditionally relies on building an explicit map that is used to plan collision-free trajectories to a desired target. In deformable, complex terrain, using geomet…
Out-of-Distribution Robustness with Deep Recursive Filters
Kapil D. Katyal, I-Jeng Wang, Gregory D. Hager
Accurate state and uncertainty estimation is imperative for mobile robots and self driving vehicles to achieve safe navigation in pedestrian rich environments. A critical component…
High-Speed Robot Navigation using Predicted Occupancy Maps
Kapil D. Katyal, Adam Polevoy, Joseph Moore +2
Safe and high-speed navigation is a key enabling capability for real world deployment of robotic systems. A significant limitation of existing approaches is the computational bottl…
Learning a Group-Aware Policy for Robot Navigation
Kapil Katyal, Yuxiang Gao, Jared Markowitz +4
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mos…
PICO: Primitive Imitation for COntrol
Corban G. Rivera, Katie M. Popek, Chace Ashcraft +3
In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO. The approach combines ideas from imitation learning, task dec…
Visual Robot Task Planning
Chris Paxton, Yotam Barnoy, Kapil Katyal +2
Prospection, the act of predicting the consequences of many possible futures, is intrinsic to human planning and action, and may even be at the root of consciousness. Surprisingly,…
Occupancy Map Prediction Using Generative and Fully Convolutional Networks for Vehicle Navigation
Kapil Katyal, Katie Popek, Chris Paxton +4
Fast, collision-free motion through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future moti…
Learning to Imagine Manipulation Goals for Robot Task Planning
Chris Paxton, Kapil Katyal, Christian Rupprecht +2
Prospection is an important part of how humans come up with new task plans, but has not been explored in depth in robotics. Predicting multiple task-level is a challenging problem…