Michael Erdmann's Research Page


Interests

Robotics

I am interested in sensing strategies that acquire object shape and configuration concurrently during manipulation. My broader interests include the mechanics of manipulation, nonprehensile manipulation, parts assembly, cooperating robots, planning under uncertainty, probabilistic strategies, sensing strategies, and automatic planning.

Robotics Motivations

I am interested in making robots act purposefully and successfully in a world in which most everything is uncertain. Sensors are noisy, actions are imprecise, and objects are often in the wrong location. Despite such obstacles to purposeful action, there are many tasks that can be accomplished successfully. Humans, animals, and some machines are proof. Providing robots with the ability to operate autonomously and purposefully requires an understanding of how different tasks may be accomplished by different repertoires of actions. Grasping, hitting, and dropping are some actions that are useful in a robot's repertoire. More exotic actions include shaking, twirling, and other actions that randomize an object's state.

My work is motivated by several desires. First, I would like to program robots more easily than is currently possible. Second, I would like to understand the scope and limitations of autonomous systems, whether biological or artificial. Third, I would like to reduce the complexity of design and planning by codifying the design parameters required to achieve a given level of automation. An underlying goal of my research is to understand the relationship between sensing, action, and prediction. In the past, I have explored various extreme points in this space. With Matt Mason I explored sensorless strategies, for my thesis work I looked at randomized strategies, and most recently I investigated fast-action minimal-sensing strategies. My research draws on tools from geometry, mechanics, planning, and stochastic processes.

See also my tenure statement.


Computational Molecular Biology

I have collaborated with Dr. Gordon Rule in the Department of Biological Sciences on a method for determining protein structure homology from sparse NMR data. More broadly, I am interested in geometric algorithms for understanding the structure and dynamics of allosteric proteins. Of particular interest to me is the extent to which topological shapes act as fingerprint identifiers of proteins. One novel result of this work is a method for representing and comparing proteins using line weavings.

For more details please see the following:         PEPMORPH         Proteins, Knots, and Line Weavings



Current Project:

Older Projects Still of Interest:



Former Students



Thesis Committee Member


Abstracts of Selected Papers

(Click here for a publication list auto-generated by the Robotics Institute.)


For related work in our laboratory take a look at:

Manipulation Lab Research Papers


We gratefully acknowledge support by the National Science Foundation for this research.

Relevant support includes a Research Initiation Award IRI-9010686 and REU supplement, a Presidential Young Investigator Award IRI-9157643, grant IRI-9213993 (with REU supplement IRI-9443084), grant IRI-9503648 (with REU supplements IRI-9642850 and IRI-9741440, and a Creativity Extension), grant IIS-9820180, and grant IIS-0222875.

Any opinions, findings, and conclusions or recommendations expressed in this research are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


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