8 citations · 21 across the 11 of their papers we have counts for
5 papers · 1 filter
Learning to Simulate Realistic LiDARs
Benoit Guillard, Sai Vemprala, Jayesh K. Gupta +4
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics mod…
COMPASS: Contrastive Multimodal Pretraining for Autonomous Systems
Shuang Ma, Sai Vemprala, Wenshan Wang +4
Learning representations that generalize across tasks and domains is challenging yet necessary for autonomous systems. Although task-driven approaches are appealing, designing mode…
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual
Kunal Menda, Jayesh K. Gupta, Zachary Manchester +1
Model-based paradigms for decision-making and control are becoming ubiquitous in robotics. They rely on the ability to efficiently learn a model of the system from data. Structured…
Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
Shushman Choudhury, Jayesh K. Gupta, Mykel J. Kochenderfer +2
We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of…
Structured Mechanical Models for Robot Learning and Control
Jayesh K. Gupta, Kunal Menda, Zachary Manchester +1
Model-based methods are the dominant paradigm for controlling robotic systems, though their efficacy depends heavily on the accuracy of the model used. Deep neural networks have be…