UMD Researchers to Have a Strong Showing at ICRA 2021

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University of Maryland researchers will present 15 papers at the IEEE International Conference on Robotics and Automation (ICRA 2021) to be held from May 30 to June 5, 2021. ICRA is the IEEE Robotics and Automation Society’s biggest conference and the premier international forum for robotics researchers to present and discuss their work. The conference will be held in a hybrid format with virtual and  in-person sessions to be held at the Xi’an International Convention and Exhibition Center in Xi’an, China. 

University of Maryland researchers, including Distinguished University Professor Dinesh Manocha (ECE/CS/UMIACS/ISR/MRC), and affiliate ECE faculty members Yiannis Aloimonos, Tom Goldstein, Ming C. Lin, Elisabeth Smela, and Pratap Tokekar, will present their work on a wide variety of topics such as deep reinforcement learning, multi-agent coordination, autonomous navigation, robot perception, and even detecting and counting oysters.

The UMD researchers continue with the trend of strong presence at the International conferences with 14 papers published at ICRA-2020 and 16 papers at IROS 2020


List of papers

0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera

C. Parameshwara, N. Sanket, C. Singh CD, C. Fermüller, Y. Aloimonos

Adaptive Tracking Control of Soft Robots using Integrated Sensing Skins and Recurrent Neural Networks

Lasitha Weerakoon, Zepeng Ye , Rahul Subramonian Bama, Elisabeth Smela, Miao Yu and Nikhil Chopra

Adversarial Differentiable Data Augmentation for Autonomous Systems

Manli Shu, Yu Shen, Ming C. Lin and Tom Goldstein

Can a Robot Trust You? A DRL-Based Approach to Personality-Driven, Human-Guided Navigation

Vishnu Sashank Dorbala, Arjun Ambalam, Aniket Bera

Communication-Aware Multi-robot Coordination with Submodular Maximization

Guangyao Shi, Md. Ishat-E-Rabban, Lifeng Zhou, and Pratap Tokekar

Detecting and Counting Oysters

Behzad Sadrfaridpour, Yiannis Aloimonos, Mia Yu, Yang Tao, and Donal Webster

DWA-RL: Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation among Mobile Obstacles

U. Patel, N. Kumar, A. Jagan, and D. Manocha

Environmental Hotspot Identification in Limited Time with a UAV Equipped with a Downward-Facing Camera

Yoonchang Sung, Deeksha Dixit, and Pratap Tokekar

Failure-Resilient Coverage Maximization with Multiple Robots

Md. Ishat-E-Rabban and Pratap Tokekar

MorphEyes: Variable Baseline Stereo For Quadrotor Navigation

N. Sanket, C. Singh, V. Asthana, C. Fermüller, Y. Aloimonos

Multi-Agent Ergodic Coverage in Urban Environments

Shivang Patel, Senthil Hariharan, Pranav Dhulipala, Ming C Lin, Dinesh Manocha, Huan "Mumu" Xu, Michael Otte

No-frills Dynamic Planning using Static Planners

Mara Levy, Vasista Ayyagari, Abhinav Shrivastava

Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization

Q. Wu, K. Xu, J. Wang, M. Xu, X. Gong, and D. Manocha

SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments

J. Choi, D. Jung, Y. Lee, D. Kim, D. Manocha, and D. Lee

SwarmCOO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms under Uncertainty

S. Arul and D. Manocha


The ECE Department thanks Richa Mathur, UMD Department of Computer Science, for the original story.

Published May 21, 2021