Speaker 1: Nialah Wilson, Cornell University
Title: Design, Coordination, and Validation of Controllers for Decision Making and Planning in Large-Scale Distributed Systems
Abstract: A good swarm will be comprised of cheap, simple robots and run on efficient algorithms, making it scalable with regards to both cost, computation, and maintenance. Previous work has been done to control large-scale distributed systems with centralized or decentralized control, but none examine what happens when modules are allowed to decide when to switch between control schemes, or explore the optimality and guarantees that can still be made in a hybrid control system. I propose using two robotic platforms, a flexible modular robot, and a team of micro blimps, to study decision making and task-oriented behaviors in large-scale distributed systems by creating new hybrid control algorithms for an extended subsumption architecture.
Speaker 2: Wil Thomason, Cornell University
Title: A Flexible Sampling-Based Approach to Integrated Task and Motion Planning
Abstract: Integrated Task and Motion Planning (TAMP) seeks to combine tools from symbolic (task) planning and geometric (motion) planning to efficiently solve geometrically constrained long-horizon planning problems. In this talk, I will present some of my work in progress on a new approach to solving the TAMP problem based on a real-valued “unsatisfaction” semantics for interpreting symbolic formulae. This semantics permits us to directly sample in regions where the preconditions for symbolic actions are satisfied. In conjunction with arbitrary task-level heuristics, this enables us to use off-the-shelf sampling based motion planning to efficiently solve TAMP problems.
Speaker 3: Ji Chen, Cornell University
Title: Verifiable Control of Robotic Swarms from High-level Specifications
Abstract: Designing controllers automatically for robotic swarm systems to guarantee safety, correctness, scalability and flexibility in achieving high-level tasks remains a challenging problem. In this talk, I will present a control scheme that takes in specifications for high-level tasks and outputs continuous controllers which result in the desired collective behaviors. In particular, I will discuss the properties that swarm must have in the continuous level to ensure the correctness of mapping from symbolic plans to real-world execution. In addition, I will also compare the centralized and decentralized approaches in terms of time efficiency, failure resilience, and computation complexity.
Tariq Iqbal, MIT
Abstract: As autonomous robots are becoming more prominent across various domains, they will be expected to interact and work with people in teams. If a robot has an understanding of the underlying dynamics of a group, then it can recognize, anticipate, and adapt to the human motion to be a more effective teammate. In this talk, I will present algorithms to measure the degree of coordination in groups and approaches to extend these understandings by a robot to enable fluent collaboration with people. I will first describe a non-linear method to measure group coordination, which takes multiple types of discrete, task-level events into consideration. Building on this method, I will then present two anticipation algorithms to predict the timings of future actions in teams. Finally, I will describe a fast online activity segmentation algorithm which enables fluent human-robot collaboration.
Bio: Tariq Iqbal is a postdoctoral associate in the Interactive Robotics Group at MIT. He received his Ph.D. from the University of California San Diego, where he was a member of the Contextual Robotics Institute and the Healthcare Robotics Lab. His research focuses on developing algorithms for robots to solve problems in complex human environments, by enabling them to perceive, anticipate, adapt, and collaborate with people.
Tom Howard, University of Rochester
Abstract: The efficiency and optimality of robot decision making is often dictated by the fidelity and complexity of models for how a robot can interact with its environment. It is common for researchers to engineer these models a priori to achieve particular levels of performance for specific tasks in a restricted set of environments and initial conditions. As we progress towards more intelligent systems that perform a wider range of objectives in a greater variety of domains, the models for how robots make decisions must adapt to achieve, if not exceed, engineered levels of performance. In this talk I will discuss progress towards model adaptation for robot intelligence, including recent efforts in natural language understanding for human-robot interaction and robot motion planning.
Biosketch: Thomas Howard is an assistant professor in the Department of Electrical and Computer Engineering at the University of Rochester. He also holds secondary appointments in the Department of Biomedical Engineering, Department of Computer Science, and Department of Neuroscience and directs the University of Rochester’s Robotics and Artificial Intelligence Laboratory. Previously he held appointments as a research scientist and a postdoctoral associate at MIT’s Computer Science and Artificial Intelligence Laboratory in the Robust Robotics Group, a research technologist at the Jet Propulsion Laboratory in the Robotic Software Systems Group, and a lecturer in mechanical engineering at Caltech and was a Goergen Institute for Data Science Center of Excellence Distinguished Researcher.Howard earned a PhD in robotics from the Robotics Institute at Carnegie Mellon University in 2009 in addition to BS degrees in electrical and computer engineering and mechanical engineering from the University of Rochester in 2004. His research interests span artificial intelligence, robotics, and human-robot interaction with particular research focus on improving the optimality, efficiency, and fidelity of models for decision making in complex and unstructured environments with applications to robot motion planning and natural language understanding. Howard was a member of the flight software team for the Mars Science Laboratory, the motion planning lead for the JPL/Caltech DARPA Autonomous Robotic Manipulation team, and a member of Tartan Racing, winner of the DARPA Urban Challenge. Howard has earned Best Paper Awards at RSS (2016) and IEEE SMC (2017), two NASA Group Achievement Awards (2012, 2014), and was a finalist for the ICRA Best Manipulation Paper Award (2012). Howard’s research at the University of Rochester has been supported by National Science Foundation, Army Research Office, Army Research Laboratory, Department of Defense Congressionally Directed Medical Research Program, and the New York State Center of Excellence in Data Science.
Matt Law, Cornell University
Steven Ceron, Cornell University
Chris Mavrogiannis, Cornell University