17 papers
PokeNet: Learning Kinematic Models of Articulated Objects from Human Observations
Anmol Gupta, Weiwei Gu, Omkar Patil +2
PokeNet is an end-to-end system that learns the kinematic models of unknown articulated objects from a single human demonstration, predicting joint parameters, manipulation order,…
StageCraft: Execution Aware Mitigation of Distractor and Obstruction Failures in VLA Models
Kartikay Milind Pangaonkar, Prabin Rath, Omkar Patil +1
Large scale pre-training on text and image data along with diverse robot demonstrations has helped Vision Language Action models (VLAs) to generalize to novel tasks, objects and sc…
You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector
Omkar Patil, Ondrej Biza, Thomas Weng +9
What happens when a pretrained generative robot policy is provided a constant initial noise as input, rather than repeatedly sampling it from a Gaussian? We demonstrate that the pe…
Simultaneous Online System Identification and Control using Composite Adaptive Lyapunov-Based Deep Neural Networks
Omkar Sudhir Patil, Emily J. Griffis, Wanjiku A. Makumi +1
Although deep neural network (DNN)-based controllers are popularly used to control uncertain nonlinear dynamic systems, most results use DNNs that are pretrained offline and the co…
Bounds on Deep Neural Network Partial Derivatives with Respect to Parameters
Omkar Sudhir Patil, Brandon C. Fallin, Cristian F. Nino +2
Deep neural networks (DNNs) have emerged as a powerful tool with a growing body of literature exploring Lyapunov-based approaches for real-time system identification and control. T…
Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation
Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah +3
Robots collaborating with humans must convert natural language goals into actionable, physically grounded decisions. For example, executing a command such as "go two meters to the…