← Search

Jonathan A. DeCastro

7 accepted papers

2025

Computational Teaching for Driving via Multi-Task Imitation Learning

ICRA 2025

Learning motor skills for sports or performance driving is often done with professional instruction from expert human teachers, whose availability is limited. Our goal is to enable automated teaching via a learned model that interacts with the student similar to a human teacher. However, training su

Cited by 4SourceScholar
2025

Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions

ICRA 2025

Non-cooperative interactions commonly occur in multi-agent scenarios such as car racing, where an ego vehicle can choose to overtake the rival, or stay behind it until a safe overtaking “corridor” opens. While an expert human can do well at making such time-sensitive decisions, autonomous agents are

Cited by 9SourceScholar
2024

A Safe Preference Learning Approach for Personalization With Applications to Autonomous Vehicles

RA-L 2024

This letter introduces a preference learning method that ensures adherence to given specifications, with an application to autonomous vehicles. Our approach incorporates the priority ordering of Signal Temporal Logic (STL) formulas describing traffic rules into a learning framework. By leveraging Pa

Cited by 8SourcecodeScholar
2021

CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy

RA-L 2021

Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this letter, we propose a novel multi-task intent recogniti

Cited by 7SourceScholar
2021

Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features

RA-L 2021

In this work, we propose a novel approach for integrating rules into traffic agent trajectory prediction. Consideration of rules is important for understanding how people behave-yet, it cannot be assumed that rules are always followed. To address this challenge, we evaluate different approaches of i

Cited by 53SourceScholar
2020

DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling

RA-L 2020

Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it - a key ability for evaluating safety from a planning and verificatio

Cited by 80SourceScholar
2015

Dynamics-driven adaptive abstraction for reactive high-level mission and motion planning

ICRA 2015poster

We present a new framework for reactive synthesis that considers the dynamics of the robot when synthesizing correct-by-construction controllers for nonlinear systems. Many high-level synthesis approaches employ discrete abstractions to reason about the dynamics of the continuous system in a simplif…

Cited by 19SourceScholar