35 citations · 39 across the 5 of their papers we have counts for
5 papers
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving
Jonathan Booher, Khashayar Rohanimanesh, Junhong Xu +5
Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning. However, these methods require l…
Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications
Zihan Zhou, Jonathan Booher, Khashayar Rohanimanesh +3
Safe reinforcement learning tasks are a challenging domain despite being very common in the real world. The widely adopted CMDP model constrains the risks in expectation, which mak…
Online Tool Selection with Learned Grasp Prediction Models
Khashayar Rohanimanesh, Jake Metzger, William Richards +1
Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped w…
Self-Supervised Goal-Conditioned Pick and Place
Coline Devin, Payam Rowghanian, Chris Vigorito +2
Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world. However, it is often not obvious \emph{how} to learning from auto…
Decision-Theoretic Planning with Concurrent Temporally Extended Actions
Khashayar Rohanimanesh, Sridhar Mahadevan
We investigate a model for planning under uncertainty with temporallyextended actions, where multiple actions can be taken concurrently at each decision epoch. Our model is based o…