Michael Zinn — accepted AI-conference papers

2022

A Method For Automated Drone Viewpoints to Support Remote Robot Manipulation

Emmanuel Senft, Michael Hagenow, Pragathi Praveena, Robert Radwin, Michael Zinn, Michael Gleicher +1

IROS 2022poster

Drones can provide a minimally-constrained adapting camera view to support robot telemanipulation. Furthermore, the drone view can be automated to reduce the burden on the operator during teleoperation. However, existing approaches do not focus on two important aspects of using a drone as an automat…

Cited by 9 Source ↗Scholar ↗

2022

Registering Articulated Objects With Human-in-the-loop Corrections

Michael Hagenow, Emmanuel Senft, Evan Laske, Kimberly Hambuchen, Terrence Fong, Robert Radwin +3

IROS 2022poster

Remotely programming robots to execute tasks often relies on registering objects of interest in the robot's environment. Frequently, these tasks involve articulating objects such as opening or closing a valve. However, existing human-in-the-loop methods for registering objects do not consider articu…

Cited by 2 Source ↗code ↗Scholar ↗

2021

Recognizing Orientation Slip in Human Demonstrations

Michael Hagenow, Bolun Zhang, Bilge Mutlu, Michael Zinn, Michael Gleicher

ICRA 2021poster

Manipulations of a constrained object often use a non-rigid grasp that allows the object to rotate relative to the end effector. This orientation slip strategy is often present in natural human demonstrations, yet it is generally overlooked in methods to identify constraints from such demonstrations…

Cited by 1 Source ↗Scholar ↗

2018

Inferring geometric constraints in human demonstrations

Guru Subramani, Michael Zinn, Michael Gleicher

CoRL 2018

This paper presents an approach for inferring geometric constraints in human demonstrations. In our method, geometric constraint models are built to create representations of kinematic constraints such as fixed point, axial rotation, prismatic motion, planar motion and others across multiple degrees

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2017

Recognizing actions during tactile manipulations through force sensing

Guru Subramani, Daniel Rakita, Hongyi Wang, Jordan Black, Michael Zinn, Michael Gleicher

IROS 2017poster

In this paper we provide a method for identifying and temporally localizing tactile force actions from measured force signals. Our key idea is to use the continuous wavelet transform (CWT) with the Complex Morlet wavelet to transform force signals into feature vectors amenable to machine learning al…

Cited by 5 Source ↗Scholar ↗

2015

Closed loop task space control of an interleaved continuum-rigid manipulator

Benjamin Conrad, Michael Zinn

ICRA 2015poster

A new manipulation approach, referred to as interleaved continuum-rigid manipulation, which combines inherently safe, flexible actuated segments with more precise embedded rigid-link joints has recently been introduced [1], [2]. The redundantly actuated manipulator possesses the safety characteristi…

Cited by 34 Source ↗Scholar ↗