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Manish Prajapat

9 accepted 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

Cited by 0Source ↗Scholar ↗
2026

Safe Exploration via Policy Priors

Manuel Wendl, Yarden As, Manish Prajapat, Anton Pollak, Stelian Coros, Andreas Krause
ICLR 2026poster

Safe exploration is a key requirement for reinforcement learning agents to learn and adapt online, beyond controlled (e.g. simulated) environments. In this work, we tackle this challenge by utilizing suboptimal yet conservative policies (e.g., obtained from offline data or simulators) as priors. Our…

Cited by 0Source ↗Scholar ↗
2025

Performance-Driven Constrained Optimal Auto-Tuner for MPC

Albert Gassol Puigjaner, Manish Prajapat, Andrea Carron, Andreas Krause, Melanie N. Zeilinger
RA-L 2025

A key challenge in tuning Model Predictive Control (<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MPC</small>) cost function parameters is to ensure that the system performance stays consistently above a certain threshold. To address this challenge, we

Cited by 6Source ↗Scholar ↗
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…

Cited by 0Source ↗code ↗Scholar ↗
2024

Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods

Riccardo De Santi, Manish Prajapat, Andreas Krause
ICML 2024poster

In classic Reinforcement Learning (RL), the agent maximizes an additive objective of the visited states, e.g., a value function. Unfortunately, objectives of this type cannot model many real-world applications such as experiment design, exploration, imitation learning, and risk-averse RL to name a f…

Cited by 7Source ↗PDF ↗Scholar ↗
2022

Near-Optimal Multi-Agent Learning for Safe Coverage Control

Manish Prajapat, Matteo Turchetta, Melanie Zeilinger, Andreas Krause
NeurIPS 2022accept

In multi-agent coverage control problems, agents navigate their environment to reach locations that maximize the coverage of some density. In practice, the density is rarely known $\textit{a priori}$, further complicating the original NP-hard problem. Moreover, in many applications, agents cannot vi…

Cited by 17Source ↗PDF ↗code ↗Scholar ↗
2021

Competitive policy optimization

Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar
UAI 2021poster

A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties. We propose competitive policy optimization (CoPO), a novel policy gradient approach that exploits the game-theoretic n…

Cited by 22Source ↗PDF ↗code ↗Scholar ↗
2019

Redundant Perception and State Estimation for Reliable Autonomous Racing

Nikhil Gosala, Andreas Bühler, Manish Prajapat, Claas Ehmke, Mehak Gupta, Ramya Sivanesan +6
ICRA 2019poster

In autonomous racing, vehicles operate close to the limits of handling and a sensor failure can have critical consequences. To limit the impact of such failures, this paper presents the redundant perception and state estimation approaches developed for an autonomous race car. Redundancy in perceptio…

Cited by 34Source ↗Scholar ↗