18 citations · 34 across the 9 of their papers we have counts for
9 papers · 1 filter
Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators
Michael Przystupa, Kerrick Johnstonbaugh, Zichen Zhang +4
Identifying an appropriate task space that simplifies control solutions is important for solving robotic manipulation problems. One approach to this problem is learning an appropri…
A Quantitative Analysis of Activities of Daily Living: Insights into Improving Functional Independence with Assistive Robotics
Laura Petrich, Jun Jin, Masood Dehghan +1
Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers focus on…
Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?
Laura Petrich, Jun Jin, Masood Dehghan +1
Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers present b…
A Geometric Perspective on Visual Imitation Learning
Jun Jin, Laura Petrich, Masood Dehghan +1
We consider the problem of visual imitation learning without human supervision (e.g. kinesthetic teaching or teleoperation), nor access to an interactive reinforcement learning (RL…
Visual Geometric Skill Inference by Watching Human Demonstration
Jun Jin, Laura Petrich, Zichen Zhang +2
We study the problem of learning manipulation skills from human demonstration video by inferring the association relationships between geometric features. Motivation for this work…
Long range teleoperation for fine manipulation tasks under time-delay network conditions
Jun Jin, Laura Petrich, Shida He +2
We present a coarse-to-fine approach based semi-autonomous teleoperation system using vision guidance. The system is optimized for long range teleoperation tasks under time-delay n…
Evaluation of state representation methods in robot hand-eye coordination learning from demonstration
Jun Jin, Masood Dehghan, Laura Petrich +2
We evaluate different state representation methods in robot hand-eye coordination learning on different aspects. Regarding state dimension reduction: we evaluates how these state r…
Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach
Jun Jin, Laura Petrich, Masood Dehghan +2
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…
Online Object and Task Learning via Human Robot Interaction
Masood Dehghan, Zichen Zhang, Mennatullah Siam +3
This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new tools and motions are taught on the fly. The robot…