# Topics > Entities > Scientific institutions >  Maryland Robotics Center, University of Maryland, College Park, Maryland, USA

## Airicist

Website - robotics.umd.edu

youtube.com/UMDRobotics

linkedin.com/company/marylandroboticscenter

instagram.com/marylandroboticscenter

Director - Derek Paley

Projects:

Baxter robots, learning to think, see, and cook

robotic cleaning

Robo Raven

RoboTerp

Optical Hand

RoboCrab, horseshoe crab

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## Airicist

Published on Apr 29, 2013




> Complete individual wing control allows crazy aerobatics like back flips and dives, previously not possible with mechanical birds! Hawk attack at 1:49.

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## Airicist

Model-Predictive Asset Defense by Team of Autonomous Surface Vehicles

Published on Oct 29, 2013




> In this video, we present a contract-based, decentralized planning approach for guarding a valuable asset by a team of autonomous unmanned surface vehicles (USV) against hostile boats in an environment with civilian traffic. The particular objective for the team of USVs is to maximize the expected time it takes a hostile boat to reach the asset. The team has to cooperatively deal with uncertainty about which boats poses an actual threat, employ active blocking to slow down the movement of boats towards the asset, and intelligently distribute themselves around the asset to optimize their future guarding opportunities. The developed planner incorporates a contract-based algorithm for allocating tasks to individual USVs through forward simulating the mission and assigning estimated utilities to candidate task allocation plans. The task allocation is based on marginal cost based contracting that allows decentralized, cooperative task negotiation among neighboring agents. The planner is capable of computing task allocation plans in real-time and is general enough to be used for a variety of scenarios. The underlying behaviors that correspond to individual tasks are optimized for two specific mission scenarios. 
> 
> E. Raboin, P. ?vec, D. Nau, and S. K. Gupta. Model-Predictive Target Defense by Team of Unmanned Surface Vehicles Operating in Uncertain Environments. IEEE International Conference on Robotics and Automation (ICRA '13), Karlsruhe, Germany, May 6-10, 2013.

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## Airicist

Published on Mar 16, 2014




> This video presents an imitation learning approach for a fluid pouring task, which consists of grasping a bottle containing a fluid and pouring a specified amount of the fluid into a container placed on a rotating table. The robot learns how to do this task from human demonstrations. In addition to learning from successful demonstrations, our approach allows learning from errors made by humans and how they recovered from these errors in subsequent trials. We collect human demonstrations of the task and label each
> demonstration as either a success or a failure. A tracking system
> is used to automatically extract motion parameters from video
> recordings of each demonstration. We present an algorithm
> that combines support vector machines (SVM) based classifiers
> and iterative search to generate initial task parameters for the robot. If the robot fails to perform using these parameters, we learn simple rules to refine them by capturing how human demonstrators change parameters to transition from failed demonstrations to successful demonstrations. We report experimental results consisting of a 5 DOF robot using the learned parameters to successfully perform the pouring task to illustrate our approach.

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## Airicist

RoboCrab: A Horseshoe Crab Inspired Amphibious Robot for Righting in Surf Zones 

 Published on Mar 23, 2014




> This video presents RoboCrab, an amphibious robot capable of traversing moderate surf zone environments. By taking inspiration from the morphology, locomotion, and righting behaviors of a horseshoe crab, the robot is designed for traversal and righting on granular terrain, open water, and turbulent surf zones.

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## Airicist

R2G2: Camouflaging, pipe-inspection, and high traction capabilities 

 Published on Apr 25, 2014




> This video demonstrates R2G2 camouflaging itself in the woods, performing pipe-inspection tasks, and slithering over tile floors and rocky terrain.

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## Airicist

Bio-inspired Robotics (ENME 489L) Fall 2013 

 Published on Apr 25, 2014




> This video shows the robots built by the students as a part of the bio-inspired robotics (ENME 489L) course taught during Fall 2013.

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## Airicist

Using Failure-to-Success Transitions in an Imitation Learning Framework 

 Published on Jul 2, 2014




> The video demonstrates an imitation learning approach for a dynamic fluid pouring task. Our approach allows learning
> from errors made by humans and how they recovered from
> these errors subsequently. We collect both successful and failed
> human demonstrations of the task. Our algorithm combines a
> support vector machine based classifier and iterative search
> to generate initial task parameters for the robot. Next, a
> refinement algorithm, capturing how demonstrators change
> parameters to transition from failure to success, enables the
> robot to address failures. Experimental results with a Baxter robot illustrate our approach.

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