Dual Control

Latest papers

8 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

When to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving
Nov 1, 2026Youran Wang, Devodita Chakravarty, Yiwei LyuDual Control Autonomous Driving Planning
the Departments of Computer Sciences and Mathematics, University of Wisconsin–Madison, USA. · the Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India. · the Department of Computer Science and Engineering, Texas A&M University, USA.
Performance Gaps Between Open-Loop, Certainty-Equivalent, and Optimal Control Under Action-Dependent Observations
Oct 24, 2026Leo Maynard-Zhang, Sunmook Choi, Yahya Sattar +3Dual Control Stochastic Optimal Control
U Washington · Cornell · Imperial College London +1
Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning
Oct 14, 2026Anutam Srinivasan, Siddhant Panse, Glen ChouDual Control Adaptive MPC
Georgia Institute of Technology, School of Electrical & Computer Engineering · Georgia Institute of Technology, Schools of Cybersecurity & Privacy, Aerospace Engineering
Belief-Informed Hybrid Control with Almost-Sure Target-Set Convergence
Oct 6, 2026Clinton Enwerem, Saleh Kemal, John S. Baras +1Belief-Space Planning Dual Control
Institute for Systems Research, University of Maryland, College Park, MD, USA
Real-Time Auto-Optimization in Unknown Environments via Structure-Exploiting Dual Control for Exploration and Exploitation
May 21, 2026Shiying Dong, Haoyang Yang, Qiwei Liu +1Dual Control Second-Order Optimization
Department of Aeronautical and Aviation Engineering and Research Centre for Low Altitude Economy (RCLAE), the Hong Kong Polytechnic University, Hong Kong, China
Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control
Apr 27, 2026Daniel Cao, Beixi Du, Andrew Lowitt +3Belief-Space Planning Dual Control
Cornell
Trajectory Planning for Safe Dual Control with Active Exploration
Apr 16, 2026Kaleb Ben Naveed, Manveer Singh, Devansh R. Agrawal +1Dual Control Safe Motion Planning
Department of Robotics, University of Michigan, Ann Arbor, MI, 48109 USA. · Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, 48109 USA.
Learning On The Job: Zero-Shot Task Execution under Parametric Uncertainty via Trajectory-Parametrized Dual Control
Oct 23, 2025Victor Vantilborgh, Hrishikesh Sathyanarayan, Guillaume Crevecoeur +2Robotic Control Online System Identification
Department of Electromechanical, Systems and Metal Engineering, Ghent University, Belgium · Department of Mechanical Engineering, Yale University, USA

Nov 1, 2026J/K move · Enter open · S save

When to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving

Youran Wang, Devodita Chakravarty, Yiwei Lyu

the Departments of Computer Sciences and Mathematics, University of Wisconsin–Madison, USA. · the Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India. · the Department of Computer Science and Engineering, Texas A&M University, USA.

Dual Control Autonomous Driving Planning Risk-Aware Path Planning Adaptive Control Adaptive MPC Belief-Space Planning+1

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Safe navigation in interactive traffic requires autonomous vehicles to reason about unobservable human characteristics and the reciprocal nature of multi-agent interactions. Traditional architectures often decouple prediction from planning, leading to the "Frozen Robot Problem" or unsafe maneuvers under high ambiguity because they treat risk assessment and information acquisition as independent objectives. We argue that the notion of safety should not remain static but must instead dynamically co-evolve with the system's internal confidence: the robot should actively clarify ambiguity when necessary while also recognizing the diminishing returns of information, and naturally transition toward task execution. We introduce a unified belief-space Model Predictive Control (MPC) framework that couples multistep information planning with a clarity-adaptive Conditional Value-at-Risk (CVaR) barrier mechanism. By treating clarity, a scalar informational state, as an explicit planning variable, the controller co-designs its probing strategy with its safety specification, tightening conservatism under high ambiguity and relaxing constraints as confidence improves. We further provide a performance analysis showing that the cost of information seeking and the expected planning-cost gap to a controller that knows the true human characteristic are bounded by residual ambiguity and solver error. We evaluate the framework in lane-merging and unsignalized-intersection scenarios against representative interactive-planning baselines. Across 5,000 simulations, the proposed method achieves the highest success rates (94.6% and 93.2%) and the lowest collision rates (1.2% and 1.4%) among the compared methods, providing a principled foundation for robust decision-making in human-centric environments.