4 papers
PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC
Mohsen Amiri, Ali Beikmohammadi, Sindri MagnuÅson +1
This paper addresses the problem of training a reinforcement learning (RL) policy under partial observability by exploiting a privileged, anytime-feasible planner agent available e…
Safe and Efficient Robot Action Planning in the Presence of Unconcerned Humans
Mohsen Amiri, Mehdi Hosseinzadeh
This paper proposes a robot action planning scheme that provides an efficient and probabilistically safe plan for a robot interacting with an unconcerned human -- someone who is ei…
REAP-T: A MATLAB Toolbox for Implementing Robust-to-Early Termination Model Predictive Control
Mohsen Amiri, Mehdi Hosseinzadeh
This paper presents a MATLAB toolbox for implementing robust-to-early termination model predictive control, abbreviated as REAP, which is designed to ensure a sub-optimal yet feasi…
Practical Considerations for Implementing Robust-to-Early Termination Model Predictive Control
Mohsen Amiri, Mehdi Hosseinzadeh
Model Predictive Control (MPC) is widely used to achieve performance objectives, while enforcing operational and safety constraints. Despite its high performance, MPC often demands…