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Found 7 papers, 4 papers with code
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyond
The perspective aims to provide a holistic picture of the autonomous advanced aerial mobility field and its future directions.
Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy
One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models.
Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning
Our MACAD-Gym platform provides an extensible set of Connected Autonomous Driving (CAD) simulation environments that enable the research and development of Deep RL- based integrated sensing, perception, planning and control algorithms for CAD systems with unlimited operational design domain under realistic, multi-agent settings.
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs).
Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks
In this paper, we leverage auxiliary information aside from raw images and design a novel network structure, called Auxiliary Task Network (ATN), to help boost the driving performance while maintaining the advantage of minimal training data and an End-to-End training method.
Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon.
