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Where did I leave my glasses? Open-vocabulary semantic exploration in real-world semi-static environments
IEEE Robotics and Automation Letters (RA-L), 2026vol. 11, no. 3, pp. 3342-3349, doi: 10.1109/LRA.2026.3656790. [pdf]
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Preventing unconstrained CBF safety filters caused by incorrect relative degree assumptions
IEEE Transactions on Automatic Control (TAC), 2026vol. 71, no. 1, pp. 700-707, doi: 10.1109/TAC.2025.3608258. [pdf]
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SM2ITH: safe mobile manipulation with interactive human prediction via task-hierarchical bilevel model predictive control
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2026doi: tbd. [pdf]
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SwarmGPT: combining large language models with safe motion planning for drone swarm choreography
IEEE Robotics and Automation Letters (RA-L), 2025vol. 10, no. 11, pp. 12237-12244, doi: 10.1109/LRA.2025.3619745. [pdf]
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Semantically safe robot manipulation: from semantic scene understanding to motion safeguards
IEEE Robotics and Automation Letters (RA-L), 2025vol. 10, no. 5, pp. 4810-4817, doi: 10.1109/LRA.2025.3553046. [pdf]
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Advancing reproducibility, benchmarks, and education with remote sim2real
IEEE Robotics and Automation Magazine (RAM), 2025vol. 32, no. 1, pp. 117-123, doi: 10.1109/MRA.2025.3527291. [pdf]
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Safe multi-agent reinforcement learning for behavior-based cooperative navigation
IEEE Robotics and Automation Letters (RA-L), 2025vol. 10, no. 6, pp. 6256-6263, doi: 10.1109/LRA.2025.3560830. [pdf]
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Perception-based hierarchical-task MPC for sequential mobile manipulation in unstructured semi-static environments
IEEE Robotics and Automation Letters (RA-L), 2025 (under review)on Jul. 27, 2025, submission #25-3280. [pdf]
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Addressing relative degree issues in control barrier function synthesis with physics-informed neural networks
arXiv Preprint, 2025available at arXiv:2504.06242. [pdf]
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Optimized control invariance conditions for uncertain input-constrained nonlinear control systems
IEEE Control Systems Letters (L-CSS), 2024vol. 8, no. 157-162, doi: 10.1109/LCSYS.2023.3344138. [pdf]
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Hierarchical task model predictive control for sequential mobile manipulation tasks
IEEE Robotics and Automation Letters (RA-L), 2024vol. 9, no. 2, pp. 1270-1277, doi: 10.1109/LRA.2023.3342671. [pdf]
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What is the impact of releasing code with publications? Statistics from the machine learning, robotics, and control communities
IEEE Control Systems Magazine (CSM), 2024vol. 44, no. 4, pp. 38-46, doi: 10.1109/MCS.2024.3402888. [pdf]
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Control-barrier-aided teleoperation with visual-inertial SLAM for safe MAV navigation in complex environments
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024pp. 17836-17842, doi: 10.1109/ICRA57147.2024.10611280. [pdf]
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Closing the perception-action loop for semantically safe navigation in semi-static environments
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024pp. 11641-11648, doi: 10.1109/ICRA57147.2024.10610267. [pdf]
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AMSwarmX: Safe swarm coordination in CompleX environments via implicit non-convex decomposition of the obstacle-free space
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024pp. 14555-14561, doi: 10.1109/ICRA57147.2024.10610428. [pdf]
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Is data all that matters? The role of control frequency for learning-based sampled-data control of uncertain systems
Proc. of the American Control Conference (ACC), 2024pp. 1249-1255, doi: 10.23919/ACC60939.2024.10644546. [pdf]
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Practical considerations for discrete-time implementations of continuous-time control barrier function-based safety filters
Proc. of the American Control Conference (ACC), 2024pp. 272-278, doi: 10.23919/ACC60939.2024.10644713. [pdf]
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Semantically safe robot manipulation: from semantic scene understanding to motion safeguard
Proc. of the Conference on Neural Information Processing Systems (NeurIPS) Workshop on Open-World Agents, 2024Extended abstract. [pdf]
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AMSwarm: an alternating minimization approach for safe motion planning of quadrotor swarms in cluttered environments
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2023pp. 1421-1427, doi: 10.1109/ICRA48891.2023.10161063. [pdf]
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Swarm-GPT: Combining large language models with safe motion planning for robot choreography design
Proc. of the Conference on Neural Information Processing Systems (NeurIPS) Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models, 2023Extended abstract. [pdf]
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Bridging the model-reality gap with Lipschitz network adaptation
IEEE Robotics and Automation Letters (RA-L), 2022vol. 7, no. 1, pp. 642-649, doi: 10.1109/LRA.2021.3131698. [pdf]
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Safe learning in robotics: from learning-based control to safe reinforcement learning
Annual Review of Control, Robotics, and Autonomous Systems, 2022vol. 5, no. 1, pp. 411-444, doi: 10.1146/annurev-control-042920-020211. [pdf]
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safe-control-gym: a unified benchmark suite for safe learning-based control and reinforcement learningIEEE Robotics and Automation Letters (RA-L), 2022vol. 7, no. 4, pp. 11142-11149, doi: 10.1109/LRA.2022.3196132. [pdf] -
Fly out the window: exploiting discrete-time flatness for fast vision-based multirotor flight
IEEE Robotics and Automation Letters (RA-L), 2022vol. 7, no. 2, pp. 5023-5030, doi: 10.1109/LRA.2022.3154008. [pdf]
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Robust predictive output-feedback safety filter for uncertain nonlinear control systems
Proc. of the IEEE Conference on Decision and Control (CDC), 2022pp. 3051-3058, doi: 10.1109/CDC51059.2022.9992834. [pdf]
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Barrier Bayesian linear regression: online learning of control barrier conditions for safety-critical control of uncertain systems
Proc. of the Annual Learning for Dynamics and Control Conference (L4DC), 2022pp. 881-892, [pdf]
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Learning to fly: a Gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control
Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021pp. 7489-7496, doi: 10.1109/IROS51168.2021.9635857. [pdf]
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RLO-MPC: Robust learning-based output feedback MPC for improving the performance of uncertain systems in iterative tasks
Proc. of the IEEE Conference on Decision and Control (CDC), 2021pp. 2183-2190, doi: 10.1109/CDC45484.2021.9682940. [pdf]
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Deep neural networks as add-on modules for enhancing robot performance in impromptu trajectory tracking
International Journal of Robotics Research (IJRR), 2020vol. 39, no. 12, pp. 1397-1418, doi: 10.1177/0278364920953902. [pdf]
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To share or not to share? Performance guarantees and the asymmetric nature of cross-robot experience transfer
IEEE Control Systems Letters (L-CSS), 2020vol. 5, no. 3, pp. 923-928, doi: 10.1109/LCSYS.2020.3005886. [pdf]
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Experience selection using dynamics similarity for efficient multi-source transfer learning between robots
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020pp. 2739-2745, doi: 10.1109/ICRA40945.2020.9196744. [pdf]
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An analysis of the expressiveness of deep neural network architectures based on their lipschitz constants
arXiv Preprint, 2020available at arXiv:1912.11511. [pdf]
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Active training trajectory generation for inverse dynamics model learning with deep neural networks
Proc. of the IEEE Conference on Decision and Control (CDC), 2019pp. 1784-1790, doi: 10.1109/CDC40024.2019.9029973. [pdf]
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Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking
Proc. of the European Control Conference (ECC), 2019pp. 1-8, doi: 10.23919/ECC.2019.8796140. [pdf]
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Knowledge transfer between robots with online learning for enhancing robot performance in impromptu trajectory tracking
Proc. of the IEEE International Conference on Robotics and Automation (ICRA) Resilient Robot Teams Workshop, 2019Extended abstract. [pdf]
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An inversion-based learning approach for improving impromptu trajectory tracking of robots with non-minimum phase dynamics
IEEE Robotics and Automation Letters (RA-L), 2018vol. 3, no. 3, pp. 1663-1670, doi: 10.1109/LRA.2018.2801471. [pdf]
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Design of deep neural networks as add-on blocks for improving impromptu trajectory tracking
Proc. of the IEEE Conference on Decision and Control (CDC), 2017pp. 5201-5207, doi: 10.1109/CDC.2017.8264430. [pdf]
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Deep neural networks as add-on modules for high-accuracy impromptu trajectory tracking
Proc. of the Conference on Robot Learning (CoRL), 2017Extended abstract. [pdf]
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A comparison of probabilistic population code and sampling-based code in neural state
estimations
Proc. of the Conference on Cognitive Computational Neuroscience (CCN), 2017Extended abstract. [pdf]