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.