Revision as of 01:37, 12 April 2021 by Utopcu
- Formal methods + controls: The methods we have developed address automated synthesis of control protocols that rely on integration of physical laws and software principles to serve in adversarial environments subject to rich temporal-logic-like specifications.
- Learning + formal methods: The central question is how we can develop autonomy protocols that not only learn from their interactions with the environment and users but also provably satisfy high-level safety and performance specifications.
- Controls + learning: The main question is how we can guarantee safety and robustness feedback control systems that integrate learning-enable components, e.g., classifiers, in the loop.
In addition to the applications in aerial and ground vehicles (and robots), we interpret autonomy broadly with other emerging applications in networks on large-scale aerospace systems and additive manufacturing.
- Leveraging Symbolic Representations for Safe and Assured Learning (DARPA)
- Center of Excellence: Assured Autonomy in Contested Environments (AFOSR)
- Control, Learning and Adaptation in Information-Constrained, Adversarial Environments (DARPA)
- Optimized and Robust Design Techniques for Hypersonic Vehicle Control (Sandia National Labs)
- Verifiable, Control-Oriented Learning On The Fly (AFOSR, MURI)
- Joint Perception and Temporal Logic Planning for Distributed Agents in Dynamic Environments (ONR)
- Data-Driven Cyberphysical Systems (NSF, CPS)
- Co-Synthesis for Self-Awareness and Reconfiguration in Networked Systems (ONR)
- Safety-Constrained and Efficient Learning for Resilient Autonomous Space Systems (NASA)
- CAREER: Provably Correct Shared Control for Human-Embedded Autonomous Systems (NSF)
- Formal Synthesis of Collaborative Protocols for Joint Learning and Control (DARPA)
- Synthesis of Correct-By-Construction Control Protocols for Open, Reconfigurable Shipboard Networks (ONR)
- Autonomous Detection and Assessment with Moving Sensors (Sandia National Labs)
- High-Confidence, Efficient Learning Under Rich Task Specifications (NSF, RI)
- Explainable and Scalable Planning with Probabilistic Temporal Logic Specifications (NASA)
- Compositional Verification of Hybrid Systems (AFRL, RQ)
- Hybridizing Learning and Model-Based Planning for Active Perception (ONR)
- Exploiting Symmetries in Software for More Robust and Efficient Systems (DARPA)
- EAGER: Human-Aware Navigation in Populated Indoor Environments (NSF, NRI)
- Probably Approximately Correct Protocols for Reactive Control and Learning (ARO)
- Formal Specification and Correct-by-Construction Synthesis of Control Protocols for Adaptable, Human-Embedded Autonomous Systems (AFRL, RQ)
- Risk-Aware, Human-Cooperative Planning for Autonomous Systems (ONR, subcontract from JPL)
- Autonomy Protocols: From Human Behavioral Modeling to Correct-By-Construction, Scalable Control (NSF, CPS)
- STTR (Phases I and II): Correct-by-Construction Synthesis for Multi-Vehicle Autonomy Missions (AFOSR, subcontract from Galois Inc.)
- Perception-Based, Reactive, Temporal-Logic Planning For Autonomous Deck Operations (ONR)
- Specification, Synthesis and Verification of Software-Based Control Protocols for Fault-Tolerant Space Systems (AFRL, RV)
- Architectural and Algorithmic Solutions for Large-Scale PEV Integration into Power Grids (NSF, CPS)
- Formal Synthesis of Software-Based Control Protocols for Fractionated, Composable Autonomous Systems (AFOSR, YIP)