39 citations · 47 across the 5 of their papers we have counts for
8 papers
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang +7
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier-T…
Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyond
Sakshi Mishra, Praveen Palanisamy
Developing aerial robots that can both safely navigate and execute assigned mission without any human intervention - i.e., fully autonomous aerial mobility of passengers and goods…
Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy
Mehrnaz Sabet, Praveen Palanisamy, Sakshi Mishra
One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models. Due to costly and time-consuming real-world dat…
Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning
Praveen Palanisamy
The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design…
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
Sakshi Mishra, Praveen Palanisamy
For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount…
Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks
Yilun Chen, Praveen Palanisamy, Priyantha Mudalige +2
A safe and robust on-road navigation system is a crucial component of achieving fully automated vehicles. NVIDIA recently proposed an End-to-End algorithm that can directly learn s…