Daniel Larsson

Interests

Research

autonomy, decision-making under uncertainty, path-planning, information-limited control, information-theoretic abstraction; representations for autonomous systems; artificial intelligence, optimization, inference and estimation, data compression, frameworks for decision and perception in resource-limited systems, information-theoretic compression

Courses

Scholarly Contributions

Chapters

• Larsson, D. T., Maity, D., & Tsiotras, P. (2023). A Linear Programming Approach for Resource-Aware Information-Theoretic Tree Abstractions. In Computation-Aware Algorithmic Design for Cyber-Physical Systems(pp 101--138). Springer International Publishing.

Journals/Publications

• Larsson, D. T., & Maity, D. (2025). Communication-Aware Hierarchical Map Compression of Time-Varying Environments for Mobile Robots. IEEE Robotics and Automation Letters, 10(Issue 12). doi:10.1109/lra.2025.3622863
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  In this letter, we develop a systematic framework for the time-sequential compression of dynamic probabilistic occupancy grids. Our approach leverages ideas from signal compression theory to formulate an optimization problem that searches for a multi-resolution hierarchical encoder that balances the quality of the compressed map (distortion) with its description size, the latter of which relates to the bandwidth required to reliably transmit the map to other agents or to store map estimates in on-board memory. The resulting optimization problem allows for multi-resolution map compressions to be obtained that satisfy available communication or memory resources, and does not require knowledge of the occupancy map dynamics. We develop an algorithm to solve our problem, and demonstrate the utility of the proposed framework in simulation on both static (i.e., non-time varying) and dynamic (time-varying) occupancy maps.
• Larsson, D. T., Maity, D., & Tsiotras, P. (2025). A Dual Approach for Hierarchical Information-Theoretic Tree Abstractions.
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  In this paper, we consider establishing a formal connection between two distinct tree-abstraction problems inspired by the information-bottleneck (IB) method. Specifically, we consider the hard- and soft-constrained formulations that have recently appeared in the literature to determine the conditions for which the two approaches are equivalent. Our analysis leverages concepts from Lagrangian relaxation and duality theory to relate the dual function of the hard-constrained problem to the Q-function employed in Q-tree search and shows the connection between tree phase transitions and solutions to the dual problem obtained by exploiting the problem structure. An algorithm is proposed that employs knowledge of the tree phase transitions to find a setting of the dual variable that solves the dual problem. Furthermore, we present an alternative approach to select the dual variable that leverages the integer programming formulation of the hard-constrained problem and the strong duality of linear programming. To obtain a linear program, we establish that a relaxation of the integer programming formulation of the hard-constrained tree-search problem has the integrality property by showing that the program constraint matrix is totally unimodular. Empirical results that corroborate the theoretical developments are presented and discussed throughout.[Journal_ref: ]
• Larsson, D. T., Maity, D., & Tsiotras, P. (2022). A Generalized Information-Theoretic Framework for the Emergence of Hierarchical Abstractions in Resource-Limited Systems. Entropy, 24(6).
• Larsson, D. T., Maity, D., & Tsiotras, P. (2021). Information-Theoretic Abstractions for Planning in Agents With Computational Constraints. IEEE Robotics and Automation Letters, 6(4), 7651-7658.
• Larsson, D. T., Maity, D., & Tsiotras, P. (2020). Q-Tree Search: An Information-Theoretic Approach Toward Hierarchical Abstractions for Agents With Computational Limitations. IEEE Transactions on Robotics, 36(6), 1669-1685.

Proceedings Publications

• Larsson, D. T., Asgharivaskasi, A., Lim, J., Atanasov, N., & Tsiotras, P. (2023). Information-theoretic Abstraction of Semantic Octree Models for Integrated Perception and Planning. In 2023 IEEE International Conference on Robotics and Automation (ICRA).
• Larsson, D. T., Maity, D., & Tsiotras, P. (2021). Information-theoretic abstractions for resource-constrained agents via mixed-integer linear programming. In Proceedings of the Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems.
• Larsson, D., Nguyen, C., & Artemiadis, P. (2020). Modeling and Control of Mid-flight Coupling of Quadrotors: A new concept for Quadrotor cooperation. In 2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020.
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  Multirotor vehicles, quadrotors specifically, have formed a fast-growing field in robotics, with the range of applications spanning from surveillance and reconnaissance to agriculture and large area mapping. Although in most applications, a single quadrotors is used, there is an increasing interest in architectures controlling multiple quadrotors executing a collaborative task. This paper introduces a new concept of control involving more than one quadrotors, according to which two quadrotors can be physically coupled in mid-flight. This concept equips the quadrotors with new capabilities, e.g. increased payload or pursuit and capturing of other quadrotors. A comprehensive analysis of the approach is presented for the system of two coupled quadrotors. The dynamics and modeling of the coupled system is presented together with a discussion regarding the coupling mechanism and the overall control architecture. Controller gains were found using Linear Quadratic Control (LQR) techniques combined with Proportional Integral Derivative (PID) gain scheduling to account for the change in system dynamics to ensure stability and satisfactory response characteristics in actual experiments. Finally, the proposed methods are evaluated through an experiment that involved physical coupling and coupled flight of a pair of quadrotors.
• Larsson, D. T., Kotsalis, G., & Tsiotras, P. (2018). Nash and Correlated Equilibria for Pursuit-Evasion Games Under Lack of Common Knowledge. In 2018 IEEE Conference on Decision and Control (CDC).
• Karavas, G. K., Larsson, D. T., & Artemiadis, P. (2017). A hybrid BMI for control of robotic swarms: Preliminary results. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, 2017-.
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  Human Swarm Interaction (HSI) is a new field which relates to the effective control of robotic swarms by human operators. The iterature has shown that the control of swarms can become quite complicated. On the other hand, Brain Machine Interfaces (BMI) can offer intuitive control in a plethora of applications where other interfaces alone (e.g. joysticks) are inadequate or impractical, e.g. for people with motor disabilities. There are multiple types of BMI, but most of them rely on the analysis of ElectroEncephaloGraphic (EEG) signals. The authors have previously shown that swarm behaviors elicit specific brain activity on human subjects that observe them. Motivated by this result, in this work, we present preliminary results of a hybrid BMI that combines information from the brain and an external device. An algorithm for extracting information from the frequency domain of EEG signals that allows integration with the manual task of using a joystick is presented. The hybrid interface shows high accuracy and robustness when used as a brain-robot interface. Moreover, it allows for continuous control variables extracted from the EEG signals. Finally, its efficacy is proven across multiple subjects, while its performance is also demonstrated in the real-time control of a swarm of quadrotors.
• Larsson, D. T., Braun, D., & Tsiotras, P. (2017). Hierarchical state abstractions for decision-making problems with computational constraints. In 2017 IEEE 56th Annual Conference on Decision and Control (CDC).