1 citations · 2 across the 10 of their papers we have counts for
10 papers
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…
A Learning-based Planning and Control Framework for Inertia Drift Vehicles
Bei Zhou, Zhouheng Li, Lei Xie +2
Inertia drift is a transitional maneuver between two sustained drift stages in opposite directions, which provides valuable insights for navigating consecutive sharp corners for au…
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…
Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles
Bei Zhou, Cheng Hu, Jun Zeng +4
Drift vehicle control offers valuable insights to support safe autonomous driving in extreme conditions, which hinges on tracking a particular path while maintaining the vehicle st…