Yunke Ao — accepted AI-conference papers

2026

Robust-Sub-Gaussian Model Predictive Control for Safe Ultrasound-Image-Guided Robotic Spinal Surgery

Yunke Ao, Manish Prajapat, Yarden As, Yassine Taoudi-Benchekroun, Fabio Carrillo, Hooman Esfandiari +3

RA-L 2026

Safety-critical control using high-dimensional sensory feedback from optical data (e.g., images, point clouds) poses significant challenges in domains like autonomous driving and robotic surgery. Control can rely on low-dimensional states estimated from high-dimensional data. However, the estimation

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2025

MIXPINN: Mixed-Material Simulations by Physics-Informed Neural Network

Xintian Yuan, Yunke Ao, Boqi Chen, Philipp Fürnstahl

IROS 2025

Simulating the complex interactions between soft tissues and rigid anatomy is critical for applications in surgical training, planning, and robotic-assisted interventions. Traditional Finite Element Method (FEM)-based simulations, while accurate, are computationally expensive and impractical for rea

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2025

SonoGym: High Performance Simulation for Challenging Surgical Tasks with Robotic Ultrasound

Yunke Ao, Masoud Moghani, Mayank Mittal, Manish Prajapat, Luohong Wu, Frederic Giraud +3

NeurIPS 2025poster

Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. By reducing operator dependency and enhancing access to complex anatomical regions, robotic ultrasound can help improve workflow efficiency. Recent studies have d…

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2024

Melting Pot Contest: Charting the Future of Generalized Cooperative Intelligence

Rakshit Trivedi, Akbir Khan, Jesse Clifton, Lewis Hammond, Edgar A. Duéñez-Guzmán, Dipam Chakraborty +20

NeurIPS 2024poster

Multi-agent AI research promises a path to develop human-like and human-compatible intelligent technologies that complement the solipsistic view of other approaches, which mostly do not consider interactions between agents. Aiming to make progress in this direction, the Melting Pot contest 2023 focu…

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2023

Marine Vessel Attitude Estimation from Coastline and Horizon

Shobhit Singhal, Yunke Ao, Deran Maas, Bruno Arsenali, Stefano Maranò

IROS 2023poster

Reliable monitoring of vessel motions is crucial for safe and efficient operation of marine vessels. Pitching and rolling motions are commonly monitored using high-grade inertial measurement units (IMUs). However, such sensors become unreliable in presence of long-lasting accelerations. In this work…

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2022

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning

Yunke Ao, Le Chen, Florian Tschopp, Michel Breyer, Roland Siegwart, Andrei Cramariuc

ICRA 2022poster

Visual-inertial sensors have a wide range of applications in robotics. However, good performance often requires different sophisticated motion routines to accurately calibrate camera intrinsics and inter-sensor extrinsics. This work presents a novel formulation to learn a motion policy to be execute…

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2020

Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning

Le Chen, Yunke Ao, Florian Tschopp, Andrei Cramariuc, Michel Breyer, Jen Jen Chung +2

CoRL 2020

Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target. In this work we present a novel approach to obtain favorable trajectories for visual-inertial syst

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