CDC 2026 Workshop: Safe and Resilient Learning-Based Control for Robotic Systems
CDC 2026 Workshop: Safe and Resilient Learning-Based Control for Robotic Systems
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Date: December 14th, Monday, 2026
Date: December 14th, Monday, 2026
Time: 8:30 - 17:30, Hawaii Standard Time
Time: 8:30 - 17:30, Hawaii Standard Time
Location: Honolulu, Hawaii, USA
Location: Honolulu, Hawaii, USA
The growing use of robots in safety-critical settings creates a pressing need for control methods that remain reliable under model errors, changing conditions, and uncertain interactions. Learning-based control can improve adaptability and task performance by extracting useful control information from data. However, its deployment is complicated by distribution shifts, incomplete safety assurances, and limited robustness outside nominal operating conditions. This workshop will explore how learning-based methods can be combined with safety- and resilience-oriented control principles to address these limitations. The program will span foundational theory, algorithmic development, and experimental validation, with particular attention to preserving learning flexibility without compromising rigorous safety requirements. Invited speakers will highlight recent advances and demonstrate how these methods have been translated from theoretical frameworks into functioning robotic platforms.
Workshop Program
Workshop Program
08:20 – 08:30
Welcoming Remarks
Welcoming Remarks
Talk Session 1: Safe Learning with Provable Guarantees
Talk Session 1: Safe Learning with Provable Guarantees
08:30 – 09:00
Talk 1: Efficient Training of Neural Network Controllers With Provable Guarantees Using Interval Methods and Contraction Analysis
Talk 1: Efficient Training of Neural Network Controllers With Provable Guarantees Using Interval Methods and Contraction Analysis
Samuel Coogan, Associate Professor
Georgia Institute of Technology
09:00 – 09:30
Talk 2: Anytime Safe Reinforcement Learning
Talk 2: Anytime Safe Reinforcement Learning
Pol Mestres, Postdoctoral Researcher
California Institute of Technology
09:30 – 10:00
Talk 3: Inverse Reinforcement Learning for Safety-Critical Robotic Systems
Talk 3: Inverse Reinforcement Learning for Safety-Critical Robotic Systems
Yijing Xie, Assistant Professor
University of Texas at Arlington
10:00 – 10:30
Coffee Break
Coffee Break
Talk Session 2: Set-Based and Barrier-Function Safety
Talk Session 2: Set-Based and Barrier-Function Safety
10:30 – 11:00
Talk 4: Set-Based Control and Learning-Enabled Planning for Resilient Autonomy
Talk 4: Set-Based Control and Learning-Enabled Planning for Resilient Autonomy
Abraham Vinod, Principal Research Scientist
Mitsubishi Electric Research Laboratories
11:00 – 11:30
Talk 5: Composable Safety Monitors for Long-Horizon Complex Tasks
Talk 5: Composable Safety Monitors for Long-Horizon Complex Tasks
Sylvia Herbert, Assistant Professor
University of California, San Diego
11:30 – 12:00
Talk 6: Safety-Critical Control via Constraints on Learned Function-Space Coefficients
Talk 6: Safety-Critical Control via Constraints on Learned Function-Space Coefficients
Adam Thorpe, Postdoctoral Researcher
University of Texas at Austin
12:00 – 14:00
Lunch Break
Lunch Break
Talk Session 3: Learning, Optimization, and Planning for Safe Autonomy
Talk Session 3: Learning, Optimization, and Planning for Safe Autonomy
14:00 – 14:30
Talk 7: Geometric Policy Optimization for Control and Estimation
Talk 7: Geometric Policy Optimization for Control and Estimation
Shahriar Talebi, Assistant Professor
University of California, Los Angeles
14:30 – 15:00
Talk 8: Safety With Learning-Based Components in-the-Loop
Talk 8: Safety With Learning-Based Components in-the-Loop
Xinyi Wang, Assistant Professor
Oakland University
15:00 – 15:30
Talk 9: Resilient Autonomy via UAV-UGV Teaming: Efficient Trajectory Optimization through Model-Algorithm Co-Design
Talk 9: Resilient Autonomy via UAV-UGV Teaming: Efficient Trajectory Optimization through Model-Algorithm Co-Design
Yue Yu, Assistant Professor
University of Minnesota Twin Cities
15:30 – 16:00
Coffee Break
Coffee Break
Talk Session 4: Resilient Autonomous Systems
Talk Session 4: Resilient Autonomous Systems
16:00 – 16:30
Talk 10: Threat Aware Control for UAVs
Talk 10: Threat Aware Control for UAVs
David Casbeer, Technical Area Lead
Air Force Research Laboratory
16:30 – 17:00
Talk 11: Stochastic Optimal Control Approach to Safe Vehicle Autonomy and Planning
Talk 11: Stochastic Optimal Control Approach to Safe Vehicle Autonomy and Planning
Kenshiro Oguri, Assistant Professor
Purdue University
17:00 – 17:30
Panel Discussion
Panel Discussion
Organizing Team
Organizing Team
Lightning Talk Abstract Submission
Lightning Talk Abstract Submission
We are pleased to announce the call for lightning talk presentations for the CDC 2026 Workshop, Safe and Resilient Learning-Based Control for Robotic Systems.
We invite contributions on topics including the theoretical foundations of safe and resilient learning-based control for robotic systems, real-time adaptation in uncertain environments, and robustness and resilience in safety-critical control. Submissions demonstrating practical applications across autonomous robotic systems—including medical robots, wearable robots, human–robot interaction systems, and multi-robot systems—are also highly encouraged.
We also welcome presentations of work accepted for CDC 2026 that is relevant to the workshop themes.
Lightning Talk Abstract Submission Deadline: Submissions received by September 30, 2026, will receive full consideration. Reviews will continue on a rolling basis until all available lightning talk slots are filled.
Lightning Talk Abstract Submission Format: Submissions require a 1-paragraph abstract only.
Lightning Talk Abstract Submission Page: Go to the Submission Page.
Lightning Talk Abstract Presentation Format: On the workshop day, each presenter will give a 3-minute lightning talk during a rapid interaction session.
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