Laura Petrich — accepted AI-conference papers

2023

Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators

Michael Przystupa, Kerrick Johnstonbaugh, Zichen Zhang, Laura Petrich, Masood Dehghan, Faezeh Haghverd +1

ICRA 2023poster

Identifying an appropriate task space can simplify solving robotic manipulation problems. One solution is deploying control algorithms in a learned low-dimensional action space. Linear and nonlinear action mapping methods have trade-offs between simplicity and the ability to express motor commands o…

Cited by 3 Source ↗Scholar ↗

2022

A Quantitative Analysis of Activities of Daily Living: Insights into Improving Functional Independence with Assistive Robotics

Laura Petrich, Jun Jin, Masood Dehghan, Martin Jagersand

ICRA 2022poster

Wheelchair-mounted robotic manipulators have the potential to help the elderly and individuals living with disabilities carry out their activities of daily living (ADLs) independently. Robotics researchers focus on assistive tasks from the perspective of various control schemes and motion types, whe…

Cited by 24 Source ↗Scholar ↗

2020

A Geometric Perspective on Visual Imitation Learning

Jun Jin, Laura Petrich, Masood Dehghan, Martin Jagersand

IROS 2020poster

We consider the problem of visual imitation learning without human kinesthetic teaching or teleoperation, nor access to an interactive reinforcement learning training environment. We present a geometric perspective to this problem where geometric feature correspondences are learned from one training…

Cited by 22 Source ↗Scholar ↗

2020

Visual Geometric Skill Inference by Watching Human Demonstration

Jun Jin, Laura Petrich, Zichen Zhang, Masood Dehghan, Martin Jagersand

ICRA 2020poster

We study the problem of learning manipulation skills from human demonstration video by inferring the association relationships between geometric features. Motivation for this work stems from the observation that humans perform eye-hand coordination tasks by using geometric primitives to define a tas…

Cited by 12 Source ↗Scholar ↗

2019

Online Object and Task Learning via Human Robot Interaction

Masood Dehghan, Zichen Zhang, Mennatullah Siam, Jun Jin, Laura Petrich, Martin Jagersand

ICRA 2019poster

This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new objects and motions are taught on the fly. The robotic system developed was one of the five finalists in the KUKA Innovation Award competition and demonstrated during the…

Cited by 32 Source ↗Scholar ↗

2019

Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach

Jun Jin, Laura Petrich, Masood Dehghan, Zichen Zhang, Martin Jagersand

ICRA 2019poster

We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL [1]) by inferring a reward model from state tra…

Cited by 19 Source ↗Scholar ↗

2019

Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting

Mennatullah Siam, Chen Jiang, Steven Lu, Laura Petrich, Mahmoud Gamal, Mohamed Elhoseiny +1

ICRA 2019poster

Video object segmentation is an essential task in robot manipulation to facilitate grasping and learning affordances. Incremental learning is important for robotics in unstructured environments. Inspired by the children learning process, human robot interaction (HRI) can be utilized to teach robots…

Cited by 108 Source ↗code ↗Scholar ↗