TR2025-104
Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning
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- "Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning", American Control Conference (ACC), July 2025.BibTeX TR2025-104 PDF
- @inproceedings{ChavezArmijos2025jul,
- author = {Chavez Armijos, Andres and Berntorp, Karl and {Di Cairano}, Stefano},
- title = {{Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-104}
- }
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- "Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning", American Control Conference (ACC), July 2025.
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MERL Contact:
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Research Areas:
Control, Dynamical Systems, Machine Learning, Optimization, Robotics
Abstract:
We present an interactive motion planner that integrates online learning of human driver preferences with parametric control barrier functions. Using stochastic models with Gaussian disturbances to capture human-driven vehicle behavior uncertainty, we update parameters in real-time parameter by Kalman filtering while ensuring safety by control barrier functions. A case study on highway lane-changing tasks demonstrates improved traffic flow, reduced disruptions, and lighter actuation effort compared to non-adaptive algorithms.
Related News & Events
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NEWS MERL researchers present 13 papers at ACC 2025 Date: July 8, 2025 - July 10, 2025
Where: Denver, USA
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Purnanand Elango; Jordan Leung; Saviz Mowlavi; Diego Romeres; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Electric Systems, Machine Learning, Multi-Physical Modeling, RoboticsBrief- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.