A record number of papers from robotics faculty at the University of California San Diego were accepted to the 2021 International Conference on Robotics and Automation taking place in Xi’an, China from May 30 to June 5.
From helping robots navigate the ER, to making it easier for autonomous drones to fly around obstacles, to teaching robots how to suture wounds, the papers demonstrate the breath and depths of robotics research taking place at the UC San Diego Contextual Robotics Institute.
“It is encouraging to see the strong growth in submissions in all areas of robotics research,” said Henrik Christensen, a professor in the Computer Science and Engineering (CSE) Department and a director of the robotics institute, which includes more than 50 faculty and more than 100 graduate students. “In spite of COVID-19 and all the challenges it presents, our research work continues in full force.”
Here are the CSE papers accepted to the conference with summaries and links:
Looking Farther in Parametric Scene Parsing with Ground and Aerial Imagery
Raghava Modhugu, Harish Rithish Sethuram, Manmohan Chandraker, C.V. Jawahar
In this paper, researchers demonstrate the effectiveness of using aerial imagery as an additional modality to overcome challenges in road scene understanding. We propose a novel architecture, Unified, that combines features from both aerial and ground imagery to infer scene attributes.
Auto-calibration Method Using Stop Signs for Urban Autonomous Driving Applications
Yunhai Han, Yuhan Liu, David Paz, Henrik Christensen
Researchers detail in this paper how they developed an approach to calibrate sensors for self-driving cars using recognition of traffic signs, such as stop signs. The approach is based on detection, geometry estimation, calibration, and recursive updating. Results from natural environments are presented that clearly show convergence and improved performance.
Social Navigation for Mobile Robots in the Emergency Department
Angelique Taylor, Sachiko Mastumoto, Wesley Xiao, and Laurel D. Riek
In this paper, the authors introduce the Safety-Critical Deep Q-Network (SafeDQN) system, a new acuity-aware navigation system for mobile robots.
Temporal Anticipation and Adaptation Methods for Fluent Human-Robot Teaming
Tariq Iqbal and Laurel D. Riek
In this paper, researchers introduce TANDEM: Temporal Anticipa- tion and Adaptation for Machines, a series of neurobiologically- inspired algorithms that enable robots to fluently coordinate with people. TANDEM leverages a human-like understanding of external and internal temporal changes to facilitate coordination.