|12:15-02:15||Group Lunch and Discussion|
|02:15-03:30||Benchmarks Discussion and Tutorial|
|03:30-04:00||Vasu Raman (Industry Perspective)|
|04:00-04:15||Poster Lightning Talks|
|05:00-06:00||Panel Discussion and Wrap Up|
Complex robot behavior requires not only paths to navigate or reach objects, but also decisions about which objects to reach, in what order, and what style of action to perform. Such decisions combine the need for continuous, collision-free motion planning with the discrete actions of task planning. Efficient algorithms exist to solve these parts in isolation; however, integrating task and motion planning presents algorithmic challenges in generality, scalability, completeness. Task-Motion Planning (TMP) is an integrated approach to this challenge which has developed in the traditional robotics community. With this workshop, we hope to strengthen connections to the AI and formal methods communities.
Challenges in TMP planning arise from the interaction of task and motion layers. Task actions affect motion planning feasibility, and motion plan feasibility dictates the ability to perform task actions. Current work on task and motion planning has achieved good performance by focusing on specific types of actions or solving expected-case scenarios, and ongoing advances are improving completeness, generality, and optimality.
The goal of this workshop is to highlight recent applications and explore new methods for combining task and motion planning, looking beyond the traditional robotics community find connections to work in AI and cyber-physical systems. We hope to identify new abstractions and algorithms for complex robot action. From this workshop, we expect participating researchers to identify and address important challenges, techniques, and benchmarks necessary for combining task and motion planning.
This workshop is intended for researchers in robotics, AI planning, and motion planning who are interested in improving the autonomy of robots for complex tasks such as manipulation.
The two main target audiences for the workshop are: (1) members actively researching new methods, future trends and open questions in task and motion planning (2) people who are interested in learning about the current state-of-the-art in order to incorporate these methods into their own projects. We strongly encourage the participation of graduate students.
Rajeev Alur is the Zisman Family Professor in Department of Computer and Information Science at the University of Pennsylvania. His research spans three subdisciplines in computer science: theoretical computer science (topics such as automata, logics, concurrency, and models of computation); formal methods in system design (topics such as computer-aided verification, software analysis, and system synthesis); and cyber-physical systems (topics such as embedded controllers, real-time systems, and hybrid systems). He is a member of Penn’s PRECISE Center and PL Club.
Georgios Fainekos is an Assistant Professor at the School of Computing, Informatics and Decision Systems Engineering at Arizona State University. He is director of the Cyber-Physical Systems Laboratory (CPSLab) and he is also affiliated with the Center for Embedded Systems (CES). He received his Ph.D. in Computer and Information Science from the University of Pennsylvania in 2008 where he was affiliated with the GRASP Lab. Prof. Fainekos holds a Diploma degree (B.Sc. & M.Sc.) in Mechanical Engineering from the National Technical University of Athens and an M.Sc. degree in Computer and Information Science from the University of Pennsylvania. Before joining ASU, he held a Postdoctoral Researcher position at NEC Laboratories America in the System Analysis & Verification Group. He is currently working in the area of Cyber-Physical Systems. In particular, his research interests include formal methods and logic, control theory and hybrid, embedded and real-time systems with applications to robotics and unmanned aerial vehicles. Prof. Fainekos is recipient of the NSF CAREER award. He was also recipient of the SCIDSE Best Researcher Junior Faculty award for 2013 and of the 2008 Frank Anger Memorial ACM SIGBED/SIGSOFT Student Award
Tomas Lozano-Perez is currently the School of Engineering Professor in Teaching Excellence at the Massachusetts Institute of Technology (MIT), USA, where he is a member of the Computer Science and Artificial Intelligence Laboratory. He has been Associate Director of the Artificial Intelligence Laboratory and Associate Head for Computer Science of MIT’s Department of Electrical Engineering and Computer Science. He was a recipient of the 2011 IEEE Robotics Pioneer Award and a 1985 Presidential Young Investigator Award. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and a Fellow of the IEEE.
His research has been in robotics (configuration-space a pproach to motion planning), computer vision (interpretation-tree approach to object recognition), machine learning (multiple-instance learning), medical imaging (computer-assisted surgery) and computational chemistry (drug activity prediction and protein structure determination from NMR & X-ray data). His current research is aimed at integrating task, motion and decision-theoretic planning for robotic manipulation.
Dan Magazzeni is Head of the Planning Group in the Department of Informatics at King’s College London, and elected member of the ICAPS Executive Council. His main research interests are in Artificial Intelligence Planning and Model Checking, with a particular focus on hybrid systems, robotics, smart cities and intelligent traffic control.
Vasu Raman is a roboticist at Zoox, Inc. Previously, she was a Senior Scientist at the the United Technologies Research Center in Berkeley, CA, and a Computing and Mathematical Sciences postdoctoral scholar at the California Institute of Technology, where she worked with Richard Murray and Sanjit Seshia.
Her research explores algorithmic methods for designing and controlling autonomous systems, guaranteeing correctness with respect to formal specifications. She focuses on safety-critical systems performing complex tasks in adversarial environments, interacting with a variety of agents. She draws on technical and creative perspectives from formal methods for software verification, hybrid systems, robotics, control and game theory.
She earned a Ph.D. in 2013 from the Department of Computer Science at Cornell University, where she was advised by Hadas Kress-Gazit and affiliated with the Autonomous Systems Lab and the LTLMoP Project. Her dissertation addressed challenges in the synthesis of provably correct control for robotics. She also holds a B.A. in Computer Science and Mathematics from Wellesley College.
Nick Roy is an associate professor of aeronautics and astronautics at Massachusetts Institute of Technology. His research has focused specifically on the problems that result from uncertainty in the world, such as sensor noise or unpredictable action outcome. Probabilistic, decision-theoretic models have proven to be ideally suited for state estimation in the face of uncertainty; he believes that such models can be equally useful for planning. For example, he has formulated algorithms for a class of planning models called Partially Observable Markov Decision Processes (POMDPs). The POMDP framework is a general model for planning with incomplete information, however, it suffers from substantial computational intractability. His contribution has been an approach for finding approximate policies for large (and currently unsolvable) POMDP models that are relevant to real world systems.
We invite researchers to present their latest results or ongoing work during our poster session.