Author name cluster

Jonathan A. DeCastro

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

ICRA Conference 2025 Conference Paper

Computational Teaching for Driving via Multi-Task Imitation Learning

Deepak E. Gopinath
Xiongyi Cui
Jonathan A. DeCastro
Emily Sumner
Jean Costa
Hiroshi Yasuda
Allison Morgan
Laporsha Dees

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 such automated teaching systems is limited by the availability of highquality annotated datasets of expert teacher and student interactions as they are difficult to collect at scale. To address this data scarcity problem, we propose an approach for training a coaching system for complex motor tasks such as high performance driving via a Multi-Task Imitation Learning (MTIL) paradigm. MTIL allows our model to learn robust representations by utilizing self-supervised training signals from more readily available non-interactive datasets of humans performing the task of interest. We validate our approach with (1) a semi-synthetic dataset created from real human driving trajectories, (2) a professional track driving instruction dataset, (3) a track-racing driving simulator human-subject study, and (4) a system demonstration on an instrumented car at a race track. Our experiments show that the right set of auxiliary machine learning tasks improves prediction of teaching instructions. Moreover, in the human subjects study, students exposed to the instructions from our teaching system improve their ability to stay within track limits, and show favorable perception of the model's interaction with them, in terms of usefulness and satisfaction.

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ICRA Conference 2025 Conference Paper

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

Haimin Hu
Jaime F. Fisac
Naomi Ehrich Leonard
Deepak E. Gopinath
Jonathan A. DeCastro
Guy Rosman

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 incapable of rapidly reasoning about complex, potentially conflicting options, leading to suboptimal behaviors such as deadlocks. Recently, the nonlinear opinion dynamics (NOD) model has proven to exhibit fast opinion formation and avoidance of decision deadlocks. However, NOD modeling parameters are oftentimes assumed fixed, limiting their applicability in complex and dynamic environments. It remains an open challenge to determine such parameters automatically and adaptively, accounting for the ever-changing environment. In this work, we propose for the first time a learning-based and game-theoretic approach to synthesize a Neural NOD model from expert demonstrations, given as a dataset containing (possibly incomplete) state and action trajectories of interacting agents. We demonstrate Neural NOD's ability to make fast and deadlock-free decisions in a simulated autonomous racing example. We find that Neural NOD consistently outperforms the state-of-the-art data-driven inverse game baseline in terms of safety and overtaking performance.

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ICRA Conference 2022 Conference Paper

Analyzing Multiagent Interactions in Traffic Scenes via Topological Braids

Christoforos I. Mavrogiannis
Jonathan A. DeCastro
Siddhartha S. Srinivasa

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 stochasticity of human behavior may hinder the identification of important interaction patterns. Our key insight is that traffic environments feature significant geometric and temporal structure, leading to highly organized collective behaviors, often drawn from a small set of dominant modes. In this work, we propose a representation based on the formalism of topological braids that can summarize arbitrarily complex multiagent behavior into a compact object of dual geometric and symbolic nature, capturing critical events of interaction. This representation allows us to formally enumerate the space of outcomes in a traffic scene and characterize their complexity. We illustrate the value of the proposed representation in summarizing critical aspects of real-world traffic behavior through a case study on recent driving datasets. We show that despite the density of real-world traffic, observed behavior tends to follow highly organized patterns of low interaction. Our framework may be a valuable tool for evaluating the richness of driving datasets, but also for synthetically designing balanced training datasets or benchmarks.

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ICML Conference 2021 Conference Paper

The Logical Options Framework

Brandon Araki
Xiao Li 0025
Kiran Vodrahalli
Jonathan A. DeCastro
Micah J. Fry
Daniela Rus

Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF’s learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps on our benchmarks. We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment.

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IROS Conference 2020 Conference Paper

Behaviorally Diverse Traffic Simulation via Reinforcement Learning

Shinya Shiroshita
Shirou Maruyama
Daisuke Nishiyama
Mario Ynocente Castro
Karim Hamzaoui
Guy Rosman
Jonathan A. DeCastro
Kuan-Hui Lee

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 challenging. This paper introduces an easily-tunable policy generation algorithm for autonomous driving agents. The proposed algorithm balances diversity and driving skills by leveraging the representation and exploration abilities of deep reinforcement learning via a distinct policy set selector. Moreover, we present an algorithm utilizing intrinsic rewards to widen behavioral differences in the training. To provide quantitative assessments, we develop two trajectory-based evaluation metrics which measure the differences among policies and behavioral coverage. We experimentally show the effectiveness of our methods on several challenging intersection scenes.

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ICRA Conference 2015 Conference Paper

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

Jonathan A. DeCastro
Vasumathi Raman
Hadas Kress-Gazit

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 simplified manner. Often, these abstractions are expensive to compute. We circumvent the need to have detailed abstractions for nonlinear systems by proposing a framework for adapting abstractions based on partial solutions to the low-level controller synthesis problem. The contribution of this paper is a reactive synthesis algorithm that makes use of our adaptation procedure to update the high-level strategy each time the non-deterministic discrete abstraction is modified. We combine this with a verified low-level controller synthesis scheme capable of automatically synthesizing controllers for a wide class of nonlinear systems. This novel synthesis framework is demonstrated on a dynamical robot executing an autonomous inspection task.

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IROS Conference 2013 Conference Paper

Guaranteeing reactive high-level behaviors for robots with complex dynamics

Jonathan A. DeCastro
Hadas Kress-Gazit

Applying correct-by-construction planning techniques to robots with complex nonlinear dynamics requires new formal analysis methods which guarantee that the requested behaviors can be achieved in the continuous space. In this paper, we construct low-level controllers that ensure the execution of a high-level mission plan. Controllers are generated using trajectory-based verification to produce a set of robust reach tubes which strictly guarantee that the required motions achieve the desired task specification. Reach tubes, computed here by solving a series of sum-of-squares optimization problems, are composed in such a way that all trajectories ensure correct highlevel behaviors. We illustrate the new method using an input-limited unicycle robot satisfying task specifications expressed in linear temporal logic.

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