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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The Fast Downward Planning System

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COMPLEXITY RESULTS FOR SAS+ PLANNING

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KNOWROB — knowledge processing for autonomous personal robots

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Semantics derived automatically from language corpora contain human-like biases

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Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

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Towards a science of integrated AI and Robotics

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Plan explicability and predictability for robot task planning

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A vision chip with complementary pathways for open-world sensing

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3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents

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The Emerging Landscape of Explainable Automated Planning & Decision Making

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Taxonomy of Trust-Relevant Failures and Mitigation Strategies

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SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning

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PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making

Yang, F., Lyu, D., Liu, B., & Gustafson, S. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/675 · Cited by 103

Task assignment of multiple agricultural machinery cooperation based on improved ant colony algorithm

Cao, R., Li, S., Ji, Y., Zhang, Z., Xu, H., Zhang, M., Li, M., & Li, H. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.105993 · Cited by 98

Explanations and trust: What happens to trust when a robot partner does something unexpected?

Lyons, J. B., Hamdan, I. A., & Vo, T. Q. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107473 · Cited by 102

Scheduling of smart intra – factory material supply operations using mobile robots

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Survey on robotic systems for internal logistics

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Integrating action knowledge and LLMs for task planning and situation handling in open worlds

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Explainable reinforcement learning for broad-XAI: a conceptual framework and survey

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Probabilistic planning with formal performance guarantees for mobile service robots

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From Automation System to Autonomous System: An Architecture Perspective

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Reasoning With Scene Graphs for Robot Planning Under Partial Observability

Amiri, S., Chandan, K., & Zhang, S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3157567 · Cited by 57

Hybrid offline and online task planning for service robot using object-level semantic map and probabilistic inference

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Short-Term Human-Robot Interaction through Conditional Planning and Execution

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AI based combined scheduling and motion planning in flexible robotic assembly lines

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Designing Up with Value-Sensitive Design: Building a Field Guide for Ethical ML Development

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A Framework for Service Robots in Smart Home: An Efficient Solution for Domestic Healthcare

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Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps

Zheng, K., Pronobis, A., & Rao, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11743 · Cited by 28

Why Humble Farmers May in Fact Grow Bigger Potatoes: A Call for Street-Smart Decision-Making in Sport

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Dynamically Constructed (PO)MDPs for Adaptive Robot Planning

Zhang, S., Khandelwal, P., & Stone, P. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11042 · Cited by 25

Balancing Explicability and Explanation in Human-Aware PlanningPreprint

Chakraborti, T., Sreedharan, S., & Subbarao Kambhampati, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00543 · Cited by 24

Hallway exploration-inspired guidance: applications in autonomous material transportation in construction sites

Yang, G., Wang, S., Okamura, H., Shen, B., Ueda, Y., Yasui, T., Yamada, T., Miyazawa, Y., Yoshida, S., Inada, Y., et al. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103758 · Cited by 23

TDM: Trustworthy Decision-Making Via Interpretability Enhancement

Lyu, D., Yang, F., Kwon, H., Dong, W., Yılmaz, L., & Liu, B. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3084290 · Cited by 24

Open-World Reasoning for Service Robots

Jiang, Y., Walker, N., Hart, J., & Stone, P. (2019) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v29i1.3541 · Cited by 23

A novel framework to improve motion planning of robotic systems through semantic knowledge-based reasoning

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Robust and efficient task scheduling for robotics applications with reinforcement learning

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Online Explanation Generation for Planning Tasks in Human-Robot Teaming

Zakershahrak, M., Gong, Z., Sadassivam, N., & Zhang, Y. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341792 · Cited by 22

Effects of Explanation Strategy and Autonomy of Explainable AI on Human–AI Collaborative Decision-making

Wang, B., Yuan, T., & Rau, P. P. (2024) · International Journal of Social Robotics · doi:10.1007/s12369-024-01132-2 · Cited by 21

From Pixels to Buildings: End-to-end Probabilistic Deep Networks for Large-scale Semantic Mapping

Zheng, K., & Pronobis, A. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967568 · Cited by 21

Expect the Unexpected: Leveraging the Human-Robot Ecosystem to Handle Unexpected Robot Failures

Honig, S., & Oron-Gilad, T. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.656385 · Cited by 21

BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors

Wang, Y., Mu, J., Huang, H., Wang, Q., Zhu, P., & Hu, Q. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3550703 · Cited by 20

Knowledge-Based Hierarchical POMDPs for Task Planning

Serrano, S. A., Santiago, E., Martínez-Carranza, J., Morales, E. F., & Sucar, L. E. (2021) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-021-01348-8 · Cited by 18

A survey of Semantic Reasoning frameworks for robotic systems

Liu, W., Daruna, A., Patel, M., Ramachandruni, K., & Chernova, S. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104294 · Cited by 20

A Novelty-Centric Agent Architecture for Changing Worlds.

Faizan, M., Sarathy, V., Tatiya, G., Goel, S., Gyawali, S., Castro, M. G., Sinapov, J., & Scheutz, M. J. (2021) · Autonomous Agents and Multi-Agent Systems · Cited by 16

A survey of knowledge‐based sequential decision‐making under uncertainty

Zhang, S., & Sridharan, M. (2022) · AI Magazine · doi:10.1002/aaai.12053 · Cited by 16

Extracting general task structures to accelerate the learning of new tasks

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REX: Designing User-centered Repair and Explanations to Address Robot Failures

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Planning with task-oriented knowledge acquisition for a service robot

Chen, K., Yang, F., & Chen, X. (2016) · International Joint Conference on Artificial Intelligence · Cited by 15

Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning

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LAAIR: A Layered Architecture for Autonomous Interactive RobotsPreprint

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Planning Beyond The Sensing Horizon Using a Learned Context

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Answerable and Unanswerable Questions in Risk Analysis with Open‐World Novelty

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A Practical Framework for Robust Decision-Theoretic Planning and Execution for Service Robots

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SO-MRS: A Multi-robot System Architecture Based on the SOA Paradigm and Ontology

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Desire-Driven Reasoning for Personal Care Robots

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REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

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Ontological framework for high-level task replanning for autonomous robotic systems

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Towards Robust One-shot Task Execution using Knowledge Graph Embeddings

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A Bayesian based Methodology for Indirect Object Search

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Task motion planning for anthropomorphic arms based on human arm movement primitives

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A Unified Topological Representation for Robotic Fleets in Agricultural Applications

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A Novel Slot-Gated Model Combined With a Key Verb Context Feature for Task Request Understanding by Service Robots

Zhang, S., Jiang, J., He, Z., Zhao, X., & Fang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2931576 · Cited by 12

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

Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming

Zakershahrak, M., Marpally, S. R., Sharma, A., Gong, Z., & Zhang, Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561762 · Cited by 12

Which tool to use? Grounded reasoning in everyday environments with assistant robots.

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Using knowledge representation and reasoning tools in the design of robots

Sridharan, M., & Gelfond, M. (2016) · University of Birmingham Research Portal (University of Birmingham) · Cited by 10

Points, Paths, and Playscapes: Large-scale Spatial Language Understanding Tasks Set in the Real World

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Agreeing to Interact

Sasabuchi, K., Ikeuchi, K., & Inaba, M. (2018) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3173386.3173390 · Cited by 10

BlueSky: Combining Task Planning and Activity-Centric Access Control for Assistive Humanoid Robots

Bayreuther, S., Jacob, F., Grotz, M., Kartmann, R., Peller-Konrad, F., Paus, F., Hannes Hartenstein, B., & Asfour, T. (2022) · ACM Symposium on Access Control Models and Technologies (SACMAT) · doi:10.1145/3532105.3535018 · Cited by 10

Intuitive Programming of Conditional Tasks by Demonstration of Multiple Solutions

Eiband, T., Saveriano, M., & Lee, D. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2935381 · Cited by 10

Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

Wang, Y., Zhang, S., & LEE, J. (2019) · Theory and Practice of Logic Programming · doi:10.1017/s1471068419000371 · Cited by 10

Monte Carlo Tree Search and GR(1) Synthesis for Robot Tasks Planning in Automotive Production Lines

Wete, E., Greenyer, J., Wortmann, A., Flegel, O., & Klein, M. (2021) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1109/models50736.2021.00039 · Cited by 9

Safe and Efficient Navigation in Extreme Environments using Semantic Belief Graphs

Ginting, M. F., Kim, S. K., Peltzer, O., Ott, J., Jung, S., Kochenderfer, M. J., & Agha–mohammadi, A. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161056 · Cited by 9

An ambient intelligence approach for learning in smart robotic environments

Bacciu, D., Rocco, M. D., Dragone, M., Gallicchio, C., Micheli, A., & Saffiotti, A. (2019) · Computational Intelligence · doi:10.1111/coin.12233 · Cited by 9

TSM: Topological Scene Map for Representation in Indoor Environment Understanding

Liao, Z., Zhang, Y., Luo, J., & Yuan, W. (2020) · IEEE Access · doi:10.1109/access.2020.3029324 · Cited by 9

Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems

Bae, S.-H., Joo, S.-H., Choi, J.-H., Pyo, J., Park, H., & Kuc, T. (2023) · Electronics · doi:10.3390/electronics12092131 · Cited by 8

What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration

Gomez, R., Sridharan, M., & Riley, H. (2020) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-019-09672-4 · Cited by 8

Active object search in an unknown large-scale environment using commonsense knowledge and spatial relations

Kim, M., & Suh, I. H. (2019) · Intelligent Service Robotics · doi:10.1007/s11370-019-00288-5 · Cited by 8

Planning robotic agent actions using semantic knowledge for a home environment

Bernardo, R. M. C., Sousa, J. M. C., & Gonçalves, P. J. S. (2021) · Intelligence & Robotics · doi:10.20517/ir.2021.10 · Cited by 8

Systems of natural-language-facilitated human-robot cooperation: A reviewPreprint

Liu, R., & Zhang, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.08269 · Cited by 8

Task and Motion Planning for Execution in the Real

Pan, T., Shome, R., & Kavraki, L. E. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3418550 · Cited by 7

Desire-Driven Reasoning Considering Personalized Care Preferences

Yang, G., Wang, S., Yang, J., & Shi, P. J. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3131338 · Cited by 7

A comprehensive review of task understanding of command-triggered execution of tasks for service robots

Xi, X., & Zhu, S. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10347-6 · Cited by 7

All about human-robot interaction

Singh, K. J., Kapoor, D. S., & Sohi, B. S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85769-7.00010-0 · Cited by 7

A Hierarchical Deliberative Architecture Framework based on Goal Decomposition

Lesire, C., Bailon-Ruiz, R., Barbier, M., & Grand, C. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981488 · Cited by 7

Explainable Robotic Plan Execution Monitoring Under Partial Observability

Çoruhlu, G., Erdem, E., & Patoğlu, V. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3123840 · Cited by 6

Building Plannable Representations with Mixed Reality

Rosen, E., Kumar, N., Gopalan, N., Ullman, D. L., Konidaris, G., & Tellex, S. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341627 · Cited by 6

Desiderata for Planning Systems in General-Purpose Service RobotsPreprint

Walker, N., Jiang, Y., Çakmak, M., & Stone, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02300 · Cited by 6

Are We On The Same Page? Hierarchical Explanation Generation for Planning Tasks in Human-Robot Teaming using Reinforcement LearningPreprint

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Chapter 5 Robotics as an Enabler of Resiliency to Disasters: Promises and Pitfalls

Wang, R., Nakhimovich, D., Roberts, F. S., & Bekris, K. E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-70370-7_5 · Cited by 5

Should I do that? using relational reinforcement learning and declarative programming to discover domain axioms

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Task-Motion Planning System for Socially Viable Service Robots Based on Object Manipulation

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Natural Language Explanations in Human-Collaborative Systems

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Learning and Reasoning for Robot Dialog and Navigation Tasks

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Plan Explainability and Predictability for Cobots.Preprint

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Proactive care architecture for care robots

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Research Trend Analysis of Multi-Task Behavior Cloning for Robot Manipulation

Park, S.-G., Ahn, W. J., Kang, S.-W., & Lim, M. (2023) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas59377.2023.10316957 · Cited by 4

A Causal Multi-armed Bandit Approach for Domestic Robots’ Failure Avoidance

Ramoly, N., Bouzeghoub, A., & Finance, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70136-3_10 · Cited by 4

A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology

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Designing Interaction for Multi-agent Cooperative System in an Office Environment

Wang, C., Hasler, S., Mühlig, M., Joublin, F., Ceravola, A., Deigmoeller, J., Fischer, L., & An, P. (2021) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3434074.3447277 · Cited by 4

Accelerating spiking neural networks with photonic reconfigurable devices

Lu, C., Xu, K., Liu, J., Ding, Y., Yu, J., Song, J., Meng, J., Wang, T., Guo, S., Jin, X., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72119-y · Cited by 3

Engaging Learners in Dialogue Interactivity Development for Mobile Robots

Baxter, P. E., Duchetto, F. D., & Hanheide, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-18141-3_12 · Cited by 3

Multi-Constraint Optimized Planning of Tasks on Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems

Zhang, K., Fei, C., Xie, B., Wang, Y., Gong, Z., Xie, C., Nguyen, T. M. T., Yao, Y., & Miao, K. (2019) · Applied Sciences · doi:10.3390/app9153010 · Cited by 3

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