Hawes, Nick

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

Kunze, L., Hawes, N., Duckett, T., Hanheide, M., & Krajnik, T. (2018) · IEEE Robotics and Automation Letters

AI-based Problem Solving and PlanningRobotics and Automated Systems

Cited by 191

Robot task planning and explanation in open and uncertain worlds

Hanheide, M., Göbelbecker, M., Horn, G. S., Pronobis, A., Sjöö, K., Aydemir, A., Jensfelt, P., Gretton, C., Dearden, R., Janicek, M., et al. (2017) · Artificial Intelligence

AI-based Problem Solving and PlanningLogic, Reasoning, and Knowledge

Cited by 145

Fault-tolerant Control of Robot Manipulators with Sensory Faults using Unbiased Active Inference

Baioumy, M., Pezzato, C., Ferrari, R., Corbato, C. H., & Hawes, N. (2021) · 2021 European Control Conference (ECC)

Fault Detection and Control SystemsEmbodied and Extended Cognition

Cited by 19

Active Inference for Fault Tolerant Control of Robot Manipulators with Sensory Faults

Pezzato, C., Baioumy, M., Corbato, C. H., Hawes, N., Wisse, M., & Ferrari, R. (2020) · Communications in Computer and Information Science

Fault Detection and Control SystemsEmbodied and Extended Cognition

Cited by 14

On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference

Baioumy, M., Lacerda, B., Duckworth, P., & Hawes, N. (2021) · Communications in Computer and Information Science

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 4

No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery

Rutherford, A., Beukman, M., Willi, T., Lacerda, B., Hawes, N., & Foerster, J. (2024) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 2

Improving Regret Approximation for Unsupervised Dynamic Environment Generation

Mead, H., Lacerda, B., Foerster, J., & Hawes, N. (2025) · Advances in Neural Information Processing Systems 38

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 0

Improving Regret Approximation for Unsupervised Dynamic Environment Generation

Mead, H., Lacerda, B., Foerster, J., & Hawes, N. (2026) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 0