Robot task planning and explanation in open and uncertain worlds

A theory of diagnosis from first principles

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An extended set of Haar-like features for rapid object detection

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Proceedings of 1995 IEEE International Conference on Robotics and Automation

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The Fast Downward Planning System

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The Fast Downward Planning System

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Towards robotic assistants in nursing homes: Challenges and results

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Experiences with an architecture for intelligent, reactive agents

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COMPLEXITY RESULTS FOR SAS+ PLANNING

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KNOWROB — knowledge processing for autonomous personal robots

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iCORPP: Interleaved commonsense reasoning and probabilistic planning on robots

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Progressive Explanation Generation for Human-robot TeamingPreprint

Zhang, Y., & Zakershahrak, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00604 · Cited by 3

Can I Do That? Discovering Domain Axioms Using Declarative Programming and Relational Reinforcement Learning

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ProTAMP: Probabilistic Task and Motion Planning Considering Human Action for Harmonious Collaboration

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Opportunistic inference and emotion in service robots

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Knowledge Acquisition and Reasoning Systems for Service Robots: A Short Review of the State of the Art

Prasad, P. K., & Ertel, W. (2020) · International Conference on Robotics and Automation Engineering (ICRAE) · doi:10.1109/icrae50850.2020.9310835 · Cited by 2

LEAF: Using Semantic Based Experience to Prevent Task Failures

Ramoly, N., Sfar, H., Bouzeghoub, A., & Finance, B. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-67361-5_44 · Cited by 2

Integrated Commonsense Reasoning and Probabilistic Planning

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Towards an Explanation Generation System for Robots: Analysis and Recommendations

Meadows, B., Sridharan, M., & Colaco, Z. (2016) · Robotics · doi:10.3390/robotics5040021 · Cited by 2

A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming

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FailRecOnt – An Ontology-Based Framework for Failure Interpretation and Recovery in Planning and Execution

Diab, M., Pomarlan, M., Stefano Borgo, A., Beßler, D., Rossel, J., Bateman, J. A., & Beetz, M. (2021) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.26092/elib/4206 · Cited by 2

AI PLANNING AND REASONING FOR A SOCIAL ASSISTIVE ROBOT

Hoteit, B., Abdallah, A., Faour, A., Awada, I. A., & Sorici, A. (2020) · Cognition and Exploratory Learning in Digital Age · doi:10.33965/celda2020_202014l018 · Cited by 2

Effective Explanations for Belief-Desire-Intention Robots: When and What to Explain

Wang, C., Calandra, R., & Klös, V. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217662 · Cited by 1

Design and implementation of a predictive algorithm for collaborative robot programming

Sobaszek, Ł. (2026) · Scientific Reports · doi:10.1038/s41598-025-34643-7 · Cited by 1

Understanding physical properties of unseen deformable objects by leveraging large-language models and robot actions

Park, C. H., Lee, B., Jung, H., Jung, H., & Nam, C. (2026) · International Journal of Advanced Robotic Systems · doi:10.1177/17298806261430024 · Cited by 1

RobKiNet: Robotic Kinematics Informed Neural Network for optimal robot configuration prediction

Peng, Y., Wang, Z., Zhang, Y., Chang, P., He, Z., Gu, K., Zhang, H., & Chen, M. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2026.105541 · Cited by 1

Hierarchical Task Planning for Power Line Flow Regulation

Wang, C., Du, Y., Huang, Y., Chang, Y., & Guo, Z. (2024) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2023.00620 · Cited by 1

Adaptive sampling-based view planning under time constraints

Kunze, L., Sridharan, M., Dimitrakakis, C., & Wyatt, J. (2017) · European Conference on Mobile Robots · doi:10.1109/ecmr.2017.8098663 · Cited by 1

Overview of Task Understanding of Unmanned Combat Systems

Liu, Q., Zhang, J., Xin, B., & Zhang, H. (2019) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isass.2019.8757706 · Cited by 1

KGGPT: Empowering Robots with OpenAI’s ChatGPT and Knowledge Graph

Mu, Z., Zhao, W., Yin, Y., Xi, X., Song, W., Gu, J., & Zhu, S. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6495-6_29 · Cited by 1

Practical Assumptions for Planning Under Uncertainty

Saborío, J. C., & Hertzberg, J. (2017) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0006189004970502

PACMAN: A Planner-Actor-Critic Architecture for Human-Centered Planning and LearningPreprint

Lyu, D., Yang, F., Liu, B., & Gustafson, S. (2019) · arXiv (Cornell University)

Towards a Theory of Intentions for Human-Robot Collaboration

Gomez, R., Sridharan, M., & Riley, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66412-1_1

A Joint Planning and Learning Framework for Human-Aided Decision-MakingPreprint

Lyu, D., Yang, F., Liu, B., & Gustafson, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07268

A Knowledge-based Multi-entity and Cooperative System Architecture

Mühlig, M., Fischer, L., Hasler, S., & Deigmoeller, J. (2020) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms49158.2020.9209421

Desire-Driven Reasoning considering Status-Based Knowledge Description for Personal Care Robots

Yang, G., Wang, S., Yang, J., & Shi, P. J. (2020) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma49215.2020.9233588

The simulation of a service robot for task planning

Cui, G., Wei, S., & Chen, X. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1887/1/012012

Efficiently exploring for human robot interaction: partially observable Poisson processes

Jovan, F., Tomy, M., Hawes, N., & Wyatt, J. (2022) · Autonomous Robots · doi:10.1007/s10514-022-10070-9

Answerable and Unanswerable Questions in Decision and Risk Analysis

Cox, L. A. (2023) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-32013-2_4

Causal-Based Approaches to Explain and Learn from Self-Extension—A Review

Marfil, R., Bustos, P., & Bandera, A. J. (2024) · Electronics · doi:10.3390/electronics13071169

Simulated Mental Imagery for Image Synthesis and its Application in Robotic Planning

Li, S. (2024) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-10515

Planning and Scheduling of Robotic Welding Processes in the Rolling Stock Industry: A Literature Review

Payami, K., Lehoux, N., Cloutier, C., & Morin, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-68634-4_24

Self-Debugging Robots: Fault recovery through reasoning and planning

Thierauf, C., & Scheutz, M. J. (2024) · Resilience Week (RWS) · doi:10.1109/rws62797.2024.10799275

Investigation & Development of Intelligent Controller to Navigate the Wheelchair Autonomous Robot

Sivakumar, B., Hariharan, R., & Mohanavel, V. (2024) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses63760.2024.10910670

An End-to-End GPT-4o Informed Pipeline for Manipulation Task Planning with Sim2Real Validation

Guo, S., Tian, T., Zhou, J., Liu, Z., Zhou, L., Zhao, R., Huang, Z., Yuan, C., & Ang, M. H. (2025) · International Conference on Control and Robotics Engineering (ICCRE) · doi:10.1109/iccre65455.2025.11093514

Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network

Peng, Y., Wang, Z., He, Z., Chang, P., Zhou, C., Yu, Y., & Chen, M. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11164159

Contextual integration of heterogeneous data in an open and opportunistic smart environment : application to humanoid robots

Ramoly, N. (2018) · theses.fr (ABES) · doi:10.70675/2baadeacza680z4483zb08dz6e47e64029be

Stochastic Robot Behaviors through Hidden Markov Models with Inputs and Outputs

Savage, J., Rosenblueth, D. A., Gutiérrez, S. M., & Negrete, M. (2026) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-026-02370-4

Dual Reactive Planning for Heterogeneous Robots With Evolving Capabilities in Unknown Environments

Zhou, Z., Li, H., & Kan, Z. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3693941

Uncertainty-aware neural-symbolic task planning with balanced exploration and execution for domestic service robot

Li, M., Zhang, Z., & Song, B. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132962

What Happened and Why? A Mixed Architecture for Planning and Explanation Generation in Robotics

Colaco, Z., & Sridharan, M. (2015)

New methods for bridging symbolic-geometric reasoning, addressing uncertainty and action learning in task planning for robotics

Hernández, A. S. (2024) · doi:10.5821/dissertation-2117-452751

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Robot Behaviour

Hanheide, M., Gretton, C., Dearden, R., Hawes, N., Wyatt, J., Pronobis, A., Aydemir, A., & Göbelbecker, M. (2012)

Combining Declarative, Procedural and Predictive Knowledge to Generate and Execute Robot Plans Efficiently and Robustly

Stulp, F., & Beetz, M. (2008) · Cited by 1

Semantic Task Planning for Service Robots in Open Worlds

Cui, G., Wei, S., & Chen, X. (2021) · Future Internet · doi:10.3390/fi13020049 · Cited by 12