AIConfPaper
← Search

Yarden As

10 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

ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning

Yarden As, Bhavya Sukhija, Lenart Treven, Carmelo Sferrazza, Stelian Coros, Andreas Krause
ICLR 2025poster

Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with their environments to learn effectively. These limitations confine RL agents to simulated environments, hindering their…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer

Yarden As, Chengrui Qu, Benjamin Unger, Dongho Kang, Max van der Hart, Laixi Shi +3
NeurIPS 2025poster

Deploying reinforcement learning (RL) safely in the real world is challenging, as policies trained in simulators must face the inevitable *sim-to-real gap*. Robust safe RL techniques are provably safe, however difficult to scale, while domain randomization is more practical yet prone to unsafe behav…

Cited by 0Source ↗Scholar ↗
2025

Safe-EF: Error Feedback for Non-smooth Constrained Optimization

Rustem Islamov, Yarden As, Ilyas Fatkhullin
ICML 2025poster

Federated learning faces severe communication bottlenecks due to the high dimensionality of model updates. Communication compression with contractive compressors (e.g., Top-$K$) is often preferable in practice but can degrade performance without proper handling. Error feedback (EF) mitigates such is…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Data-Efficient Task Generalization via Probabilistic Model-Based Meta Reinforcement Learning

Arjun Bhardwaj, Jonas Rothfuss, Bhavya Sukhija, Yarden As, Marco Hutter, Stelian Coros +1
RA-L 2024

We introduce PACOH-RL, a novel model-based Meta-Reinforcement Learning (Meta-RL) algorithm designed to efficiently adapt control policies to changing dynamics. PACOH-RL meta-learns priors for the dynamics model, allowing swift adaptation to new dynamics with minimal interaction data. Existing Meta-R

Cited by 10Source ↗Scholar ↗
2024

Transductive Active Learning: Theory and Applications

Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause
NeurIPS 2024poster

We study a generalization of classical active learning to real-world settings with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region. We analyze a family of decision rules that sample adaptively to min…

Cited by 4Source ↗PDF ↗Scholar ↗
2024

When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL

Lenart Treven, Bhavya Sukhija, Yarden As, Florian Dorfler, Andreas Krause
NeurIPS 2024poster

Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP). However, various systems are inherently continuous in time, making discrete-time MDPs an inexact modeling choice. In many applications, such as greenhouse control or medical treatments, each…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2022

Constrained Policy Optimization via Bayesian World Models

Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause
ICLR 2022spotlight

Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based approach for policy optimization in safety critical tasks modeled via constrained Markov decision processes. Our approach…

Cited by 69Source ↗PDF ↗code ↗Scholar ↗