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Jonathan DeCastro

9 accepted papers

2025

Estimating cognitive biases with attention-aware inverse planning

Sounak Banerjee, Daphne Cornelisse, Deepak Edakkattil Gopinath, Emily Sumner, Jonathan DeCastro, Guy Rosman +2
NeurIPS 2025spotlight

People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention to objects in their environment will be biased in a way that systematically affects how they perform everyday tasks suc…

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2025

Safety with Agency: Human-Centered Safety Filter with Application to AI-Assisted Motorsports

Donggeon David Oh, Justin Lidard, Haimin Hu, Himani Sinhmar, Elle Lazarski, Deepak Edakkattil Gopinath +5
RSS 2025poster

Recent advances in safe autonomy open new opportunities in assisting humans in safety-critical and time-sensitive tasks such as motorsports. However, existing safe control algorithms predominantly focus on fully automated settings and often undermine key requirements in human–AI shared control domai…

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2024

Blending Data-Driven Priors in Dynamic Games

Justin Lidard, Haimin Hu, Asher Hancock, Zixu Zhang, Albert Gimo Contreras, Vikash Modi +6
RSS 2024poster

As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motion planning remains an open question. Existing dyna…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2024

Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing

Jonathan DeCastro, Andrew Silva, Deepak Gopinath, Emily Sumner, Thomas Matrai Balch, Laporsha Dees +1
CoRL 2024poster

Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates must react to cues of a human teammate's tactical objective to assist in a way that is consistent with the objective…

Cited by 2Source ↗Scholar ↗
2022

Analyzing Multiagent Interactions in Traffic Scenes via Topological Braids

Christoforos Mavrogiannis, Jonathan DeCastro, Siddhartha S. Srinivasa
ICRA 2022poster

We focus on the problem of analyzing multiagent interactions in traffic domains. Understanding the space of behavior of real-world traffic may offer significant advantages for algorithmic design, data-driven methodologies, and bench-marking. However, the high dimensionality of the space and the stoc…

Cited by 8Source ↗Scholar ↗
2021

Learning A Risk-Aware Trajectory Planner From Demonstrations Using Logic Monitor

Xiao Li, Jonathan DeCastro, Cristian Ioan Vasile, Sertac Karaman, Daniela Rus
CoRL 2021poster

Risk awareness is an important factor to consider when deploying policies on robots in the real-world. Defining the right set of risk metrics can be difficult. In this work, we use a differentiable logic monitor that keeps track of the environmental agents' behaviors and provides a risk metric that…

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2020

Behaviorally Diverse Traffic Simulation via Reinforcement Learning

Shinya Shiroshita, Shirou Maruyama, Daisuke Nishiyama, Mario Ynocente Castro, Karim Hamzaoui, Guy Rosman +3
IROS 2020poster

Traffic simulators are important tools in autonomous driving development. While continuous progress has been made to provide developers more options for modeling various traffic participants, tuning these models to increase their behavioral diversity while maintaining quality is often very challengi…

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2020

Differentiable Logic Layer for Rule Guided Trajectory Prediction

Xiao Li, Guy Rosman, Igor Gilitschenski, Jonathan DeCastro, Cristian-Ioan Vasile, Sertac Karaman +1
CoRL 2020

In this work, we propose a method for integration of temporal logic formulas into a neural network. Our main contribution is a new logic optimization layer that uses differentiable optimization on the formulas’ robustness function. This allows incorporating traffic rules into deep learning based tra

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