10 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…
A Physical Agentic Loop for Language-Guided Grasping with Execution-State Monitoring
Wenze Wang, Mehdi Hosseinzadeh, Feras Dayoub
Robotic manipulation systems that follow language instructions often execute grasp primitives in a largely single-shot manner: a model proposes an action, the robot executes it, an…
A Block-Alternating Iterative Approach for a Class of Non-Convex Optimization Problems
Anran Li, John P. Swensen, Mehdi Hosseinzadeh
Constrained non-convex optimization problems frequently arise in control applications. Solving such problems is inherently challenging, as existing methods often converge to subopt…
An Adaptive Method for Contextual Stochastic Multi-armed Bandits with Rewards Generated by a Linear Dynamical System
Jonathan Gornet, Mehdi Hosseinzadeh, Bruno Sinopoli
Online decision-making can be formulated as the popular stochastic multi-armed bandit problem where a learner makes decisions (or takes actions) to maximize cumulative rewards coll…
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…