17 citations · 32 across the 11 of their papers we have counts for
11 papers
Embedding Physics Priors in Robot Learning: A Survey
Mattia Piccinini, Lucas Schulze, Alice Plebe +10
The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remar…
Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC
Baha Zarrouki, Arslan Thobani, Jasper Hoffmann +6
In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-depen…
SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing
Zhouheng Li, Fangguo Zhao, Mattia Piccinini +6
Autonomous multi-vehicle racing requires real-time planning of diverse competitive behaviors in intense interactions. Existing planners often struggle to balance strategic diversit…
Enhancing Physical Consistency in Lightweight World Models
Dingrui Wang, Zhexiao Sun, Zhouheng Li +8
A major challenge in deploying world models is the trade-off between size and performance. Large world models can capture rich physical dynamics but require massive computing resou…
Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
Bei Zhou, Baha Zarrouki, Mattia Piccinini +3
Autonomous drifting is a complex and crucial maneuver for safety-critical scenarios like slippery roads and emergency collision avoidance, requiring precise motion planning and con…
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang +12
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing hav…
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control
Baha Zarrouki, Marios Spanakakis, Johannes Betz
Determining the optimal cost function parameters of Model Predictive Control (MPC) to optimize multiple control objectives is a challenging and time-consuming task. Multiobjective…
RNMPC: A Real-Time Reduced Robustified Nonlinear Model Predictive Control with Ellipsoidal Uncertainty Sets for Autonomous Vehicle Motion Control
Baha Zarrouki, João Nunes, Johannes Betz
In this paper, we present a novel Reduced Robustified NMPC (RNMPC) algorithm that has the same complexity as an equivalent nominal NMPC while enhancing it with robustified cons…
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control
Baha Zarrouki, Chenyang Wang, Johannes Betz
In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive Stochastic Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty handling, constraints rob…
A Stochastic Nonlinear Model Predictive Control with an Uncertainty Propagation Horizon for Autonomous Vehicle Motion Control
Baha Zarrouki, Chenyang Wang, Johannes Betz
Employing Stochastic Nonlinear Model Predictive Control (SNMPC) for real-time applications is challenging due to the complex task of propagating uncertainties through nonlinear sys…
EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application
Phillip Karle, Tobias Betz, Marcin Bosk +31
While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an ent…