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{ "count": 2, "next": null, "previous": null, "results": [ { "id": 72452, "title": "MechE Colloquium: From Motion to Mission Planning via Augmented Graphs of Convex Sets", "slug": "meche-colloquium-from-motion-to-mission-planning-v", "event_url": "https://memento.epfl.ch/event/meche-colloquium-from-motion-to-mission-planning-v", "visual_url": "https://memento.epfl.ch/image/33700/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/33700/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/33700/max-size.jpg", "lang": "en", "start_date": "2026-10-06", "end_date": "2026-10-06", "start_time": "12:00:00", "end_time": "13:00:00", "description": "<strong>Abstract: </strong>Robot motion planning has traditionally focused on navigating obstacle-laden environments: computing smooth, collision-free trajectories from start to goal. Many real-world missions, however, require satisfying logical precedence constraints: collecting resources before accessing restricted zones, completing subtasks in a prescribed order, or acquiring tools before they can be used. This talk presents the augmented graph of convex sets (augmented GCS) framework, which unifies continuous trajectory optimization and combinatorial task sequencing within a single optimization problem.<br>\r\nThe key insight is that a layered augmented GCS, built on an exact convex partition of the free space, simultaneously selects an optimal task completion sequence and computes an optimal continuous trajectory. A shortest path in the augmented GCS solves both problems at once, yielding an exact solution up to a finite Bézier curve parameterization. The layered structure of the augmented GCS turns out to implement precisely the Bellman-Held-Karp (BHK) dynamic programming algorithm for the Traveling Salesman Problem, establishing a formal correspondence between our framework and the combinatorial TSP literature. This makes augmented GCS a continuous-geometry generalization of BHK, achieving the same singly exponential worst-case complexity, an exponential improvement over general-purpose temporal logic tools. We further develop a library of mission specification variations (including ordered collection, disjunctive keys, conjunctive doors, timed constraints, and conditional logic) each with proven correctness, substantially expanding the range of expressible mission types. Numerical experiments confirm exponential speedups in practice and near-global-optimality on a large benchmark suite.<br>\r\nTime permitting, I will briefly discuss ongoing extensions, including spacetime augmented GCS for dynamic environments, safety-aware planning via conformal prediction sets, and a galactic survey benchmark inspired by space mission planning that stress-tests the framework at scale.<br>\r\n<br>\r\n<br>\r\n<br>\r\n<strong>Biography: </strong>Tyler Summers is an associate professor at the University of Texas at Dallas. Prior to joining UT Dallas, he was an ETH Postdoctoral Fellow at the Automatic Control Laboratory at ETH Zurich from 2011 to 2015. He received a PhD degree in Aerospace Engineering at the University of Texas at Austin in 2010. He was a Fulbright Postgraduate Scholar at the Australian National University in Canberra, Australia in 2007-2008. He received the National Science Foundation CAREER Award in 2021 and a Young Investigator Program award from the US Army Research Office in 2017. His research interests are in feedback control, optimization, and learning in complex dynamical networks, with applications in robotics and power networks.", "image_description": "", "creation_date": "2026-08-26T16:15:39", "last_modification_date": "2026-08-26T16:19:15", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "MED 0 1418", "url_place_and_room": "https://plan.epfl.ch/?room==MED%200%201418", "url_online_room": "https://epfl.zoom.us/j/61360740951", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "<a href=\"https://me.utdallas.edu/faculty/tyler-summers/\">Prof. Tyler Summers</a>, <a href=\"https://me.utdallas.edu/\">Mechanical engineering,</a> <a href=\"https://www.utdallas.edu/\">The University of Texas at Dallas</a>", "organizer": "<a href=\"https://people.epfl.ch/maryam.kamgarpour\">Prof. Maryam Kamgarpour</a>", "contact": "<a href=\"mailto:[email protected]\">Institute of Mechanical Engineering</a>", "is_internal": "False", "theme": "", "vulgarization": { "id": 1, "fr_label": "Tout public", "en_label": "General public" }, "registration": { "id": 3, "fr_label": "Entrée libre", "en_label": "Free" }, "keywords": "MechE Colloquium: From Motion to Mission Planning via Augmented Graphs of Convex Sets", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/121601/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/232/?format=api", "https://memento.epfl.ch/api/v1/mementos/270/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/417/?format=api", "https://memento.epfl.ch/api/v1/mementos/394/?format=api" ] }, { "id": 72758, "title": "Safe Guaranteed Exploration for Non-linear Systems", "slug": "safe-guaranteed-exploration-for-non-linear-systems", "event_url": "https://memento.epfl.ch/event/safe-guaranteed-exploration-for-non-linear-systems", "visual_url": "https://memento.epfl.ch/image/33973/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/33973/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/33973/max-size.jpg", "lang": "en", "start_date": "2026-12-04", "end_date": "2026-12-04", "start_time": "14:00:00", "end_time": "15:00:00", "description": "<strong>Abstract</strong>: <br>\r\nSafely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficient exploration are also crucial for ensuring autonomous task completion. To address these challenges, we propose a novel safe guaranteed exploration framework using optimal control, which achieves first-of-its-kind results: guaranteed exploration for non-linear systems with finite time sample complexity bounds, while being provably safe with arbitrarily high probability. The framework is general and applicable to many real-world scenarios with complex non-linear dynamics and unknown domains. For efficient implementation, we exploit goal-directed exploration, and receding-horizon replanning while preserving the framework’s guarantees, and demonstrate safe, efficient exploration in challenging unknown environments using a car model.<br>\r\n<br>\r\n<strong>Biography</strong>: <br>\r\nManish Prajapat earned his Ph.D. in reinforcement learning and control from ETH Zurich in May 2026. He was a Doctoral Fellow at the ETH AI Center working with Prof. Melanie Zeilinger and Prof. Andreas Krause. Previously, he earned his master’s degree in Robotics, Systems, and Control from ETH Zurich and was a visiting scholar at Caltech. He received his bachelor’s degree from the Indian Institute of Technology (IIT) Madras in 2017. At IIT Madras, he was honored as the Best Graduating Student (Co-curricular) in 2017 and received the Sivasailam Merit Prize for the best thesis. His research interests are sequential decision-making under complex scenarios, e.g., non-Markovian objectives, unknown constraints or unknown dynamics of non-linear systems.<br>\r\n<br>\r\n ", "image_description": "", "creation_date": "2026-10-02T16:39:32", "last_modification_date": "2026-10-02T16:47:13", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "ME C2 405", "url_place_and_room": "https://plan.epfl.ch/?room==ME%20C2%20405", "url_online_room": "https://epfl.zoom.us/j/69006439273", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "Dr Manish Prajapat Ph.D. reinforcement learning and control from <a href=\"https://control.ee.ethz.ch\">ETH Zurich</a>\r\n\r\n<a href=\"https://ieee.ch/chapters/control-systems-society/\">Winner of the IEEE CSS Young Author Best Journal Paper Award 2026</a>", "organizer": "<a href=\"https://people.epfl.ch/giancarlo.ferraritrecate\">Prof Giancarlo Ferrari Trecate</a> The seminar is sponsored by the Swiss chapter of the <a href=\"https://ieee.ch\">IEEE-CSS</a>", "contact": "[email protected]", "is_internal": "False", "theme": "", "vulgarization": { "id": 1, "fr_label": "Tout public", "en_label": "General public" }, "registration": { "id": 3, "fr_label": "Entrée libre", "en_label": "Free" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/122052/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/15/?format=api", "https://memento.epfl.ch/api/v1/mementos/394/?format=api" ] } ] }
