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Cognitive Robotics | Maryland Robotics Center
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Cognitive Robotics
Endowing a robot with intelligent behavior by providing it with a processing architecture that allows it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robots integrate perception, cognition and action.
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Videos
Cognitive Robotics Playlist
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Extremum Seeking Controlled Wiggling for Tactile Insertion -
FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation -
AcTExplore: Active Tactile Exploration on Unknown Objects -
VAIolin — Music Education for All -
Autonomous Social Distancing in Urban Environments using a Quadruped Robot -
COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios -
Crowd-Steer: Smooth and Collision-Free Navigation in Dense Scenarios Using High-Fidelity Simulation -
ProxEmo: Gait-based Emotion Learning and Multiview Proxemic Fusion for Social-Aware Robot Navigation -
GraphRQI: Classifying Driver Behaviors Using Graph Spectrums -
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding -
DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors -
ENEE408I Squats -
EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras [ICRA 2020] -
Robot navigation in dense crowds -
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras -
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion from Sparse Event Data [Updated] -
Robotic Imitation -
GapFlyt: Active Vision Based Minimalist Structureless Gap Detection For Quadrotor Flight (IROS 2018) -
microwave demo -
Realtime Pedestrian Behavior Learning for Path Prediction and Navigation -
Compliant Object Cleaning -
Visual processes of action recognition -
UMD robot that learns to cook by watching YouTube video -
UMD robot learns to make a beverage by observing people. -
Robotic Cleaning by Scrubbing -
Bimanual Robotic Cleaning of Compliant Parts -
New Robot Sensing Hand for Co-robotics -
RoboSAM: Robotic Smart Assistant for Manufacturing -
Baxter Robot Learning to Pour into a Moving Container -
Using Failure-to-Success Transitions in an Imitation Learning Framework -
Imitation Learning of Motion Parameters for Dynamic Manipulation Tasks -
Semiautonomous Teleoperation -
Stable with input output delay -
Private video
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Private video
Faculty
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John S. BarasDistinguished University Professor301.405.6606 | baras@umd.edu Profile |
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Cornelia FermüllerResearch Scientist301.405.1768 | fermulcm@umd.edu Profile |
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Don PerlisProfessor Emeritus301-405-2685 | perlis@cs.umd.edu Profile |
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James A. ReggiaProfessor Emeritus301-405-2686 | reggia@cs.umd.edu Profile |
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Ramani DuraiswamiProfessor(301)405-6710 | ramani@umiacs.umd.edu Profile |
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Dinesh ManochaDistinguished University Professor301.405.2741 | dmanocha@umd.edu Profile |
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Yiannis AloimonosProfessor301.405.1743 | jyaloimo@umd.edu Profile |
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Ruohan GaoAssistant Professor| rhgao@umd.edu Profile |
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