About us

We are the Control & Trustworthy Robotics Lab (CTRL) at the University of Washington, a research lab developing trustworthy autonomous systems that can operate seamlessly with, alongside, and around humans.

ctrl

CTRL focuses on developing safe, intelligent, and trustworthy robotics systems that can operate seamlessly with, alongside, and around humans. Our work is at the intersection of control theory, machine learning, robotics, and formal methods. The ultimate goal is to achieve human trust in learning-enabled robot autonomy, starting from the algorithmic foundations of safe robot decision-making and control, and incorporating further refinement through learnings from practical deployment.

Research Areas

Below are research thrusts that CTRL is working on:

Safe Interaction-aware Planning and Control

The next wave of robotic systems will operate in dynamic, stochastic, and unstructured environments, especially settings where robots must interact with humans. This line of research develops techniques to model human interaction dynamics and provide safety assurances throughout a potentially learning-enabled robot autonomy stack to enable intelligent yet safe operations.

Relevant works
  1. Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction
    Schmerling, E., Leung, K., Vollprecht, W., and Pavone, M.
    In Proc. IEEE Conf. on Robotics and Automation, 2018
  2. Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
    Ivanovic, B.*, Leung, K.*, Schmerling, E., and Pavone, M.
    IEEE Robotics and Automation Letters, 2021
  3. On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
    Leung, K., Schmerling, E., Zhang, M., Chen, M., Talbot, J., Gerdes, J. C., and Pavone, M.
    Int. Journal of Robotics Research, 2020
  4. Legible and Proactive Robot Planning for Prosocial Human-Robot Interactions
    Geldenbott, J., and Leung, K.
    In Proc. IEEE Conf. on Robotics and Automation, 2024
  5. CoBL-Diffusion: Diffusion-Based Conditional Robot Planning in Dynamic Environments Using Control Barrier and Lyapunov Functions
    Mizuta, K., and Leung, K.
    In IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2024

Structured Reasoning for Robot Learning

As the field of robot autonomy continues to harness the power of data and deep learning, it also becomes increasingly difficult to explain a robot’s behavior and interpret its decision-making process, necessary prerequisites for robots operating in safety-critical settings. This line of research develops techniques that infuse structured reasoning into robot learning to enable more interpretable and robust algorithms.

Relevant works
  1. Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods
    Leung, K., Aréchiga, N., and Pavone, M.
    Int. Journal of Robotics Research, 2022
  2. Towards the Unification and Data-Driven Synthesis of Autonomous Vehicle Safety Concepts
    Leung, K.*, Bajcsy, A.*, Schmerling, E., and Pavone, M.
    (preprint)
  3. Learning Autonomous Vehicle Safety Concepts from Demonstrations
    Leung, K., Veer, S., Schmerling, E., and Pavone, M.
    In American Control Conference, 2023
  4. Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving
    Cosner, R., Chen, Y., Leung, K., and Pavone, M.
    In Proc. IEEE Conf. on Robotics and Automation, 2023
  5. Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions
    Remy, I., Fridovich-Keil, D., and Leung, K.
    In American Control Conference, 2025 (in print)
  6. STLCG++: A Masking Approach for Differentiable Signal Temporal Logic Specification
    Kapoor, P., Mizuta, K., Kang, E., and Leung, K.
    2025 (submitted)

Real-time decision-making and control for safety-critical operations

Autonomous decision-making in safety-critical scenarios require fast and efficient techniques to understand the environment, reasoning about safety risks, and calculating safe and feasible trajectories. This line of research focuses on developing real-time algorithms and techniques that enable autonomous systems to safely and swiftly operate in safety-critical scenarios, such as autonomous driving and planetary landing.

Relevant works
  1. HALO: Hazard-Aware Landing Optimization for Autonomous Systems
    Hayner, C. R., Buckner, S. C., Broyles, D., Madewell, E., Leung, K., and Açıkmeşe, B.
    In Proc. IEEE Conf. on Robotics and Automation, 2023
  2. Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles
    Veer, S., Leung, K., Cosner, R., Chen, Y., and Pavone, M.
    In Proc. IEEE Conf. on Robotics and Automation, 2023
  3. Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition
    Topan, S., Chen, Y., Schmerling, E., Leung, K., Nilsson, J., Cox, M., and Pavone, M.
    In IEEE Intelligent Vehicles Symposium, 2023
  4. Continuous-Time Line-of-Sight Constrained Trajectory Planning for 6-Degree of Freedom Systems
    Hayner, C. R., Carson, J., Açıkmeşe, B., and Leung, K.
    IEEE Robotics and Automation Letters, 2025 (in print)