34 citations · 77 across the 27 of their papers we have counts for
13 papers · 1 filter
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning
Pingcheng Jian, Xiao Wei, Yanbaihui Liu +3
We introduce Large Language Model-Assisted Preference Prediction (LAPP), a novel framework for robot learning that enables efficient, customizable, and expressive behavior acquisit…
Human-in-the-Loop Robot Planning with Non-Contextual Bandit Feedback
Yijie Zhou, Yan Zhang, Xusheng Luo +1
In this paper, we consider a robot navigation problem in environments populated by humans. The goal is to determine collision-free and dynamically feasible trajectories that also m…
Socially-Aware Robot Planning via Bandit Human Feedback
Xusheng Luo, Yan Zhang, Michael M. Zavlanos
In this paper, we consider the problem of designing collision-free, dynamically feasible, and socially-aware trajectories for robots operating in environments populated by humans.…
An Abstraction-Free Method for Multi-Robot Temporal Logic Optimal Control Synthesis
Xusheng Luo, Yiannis Kantaros, Michael M. Zavlanos
The majority of existing Linear Temporal Logic (LTL) planning methods rely on the construction of a discrete product automaton, that combines a discrete abstraction of robot mobili…
Deep Learning for Robotic Mass Transport Cloaking
Reza Khodayi-mehr, Michael M. Zavlanos
We consider the problem of mass transport cloaking using mobile robots. The robots move along a predefined curve that encloses a safe zone and carry sources that collectively count…
Physics-Based Learning for Robotic Environmental Sensing
Reza Khodayi-mehr, Michael M. Zavlanos
We propose a physics-based method to learn environmental fields (EFs) using a mobile robot. Common purely data-driven methods require prohibitively many measurements to accurately…