Break

Klaus Building Robotics Lab Tour

Departure Time: 11:40 AM Return Time: 2:00 PM Capacity: 15 Touring of the robotics labs at Klaus Advanced Computing Building (KACB)

Lunch

Advanced Manufacturing Pilot Facility 2

Departure Time: 12:45 PM Return Time: 3:00 PM Capacity: 20 High Bay user facility of the Georgia Tech Manufacturing Institute focused on translating manufacturing 4.0 technologies into industry adoption, with a focus on additive metal manufacturing, FIS, and AI in manufacturing.

Georgia Tech Research Institute 2

Departure Time: 12:45 PM Return Time: 3:00 PM Capacity: 20 Presentation of Georgia Tech Research Institute research labs and demonstration of robotics applications including multi-robot collaborative manipulation, VR tasking, adaptive cutting, and robotics for animal systems.

Georgia Tech Hi-bay Robotics and Human-Augmentation Space 2

Departure Time: 12:45 PM Return Time: 3:00 PM Capacity: 45 Group will be split in 3 and rotated through 3 lab areas: 1) CAREN area 2) Overground motion lab area 3) Autonomous legged robotics area. Each area will host 15 people per time, and 30 minutes per session

Klaus Building Robotics Lab Tour 2

Departure Time: 1:00 PM Return Time: 3:30 PM Capacity: 15 Touring of the robotics labs at Klaus Advanced Computing Building (KACB)

Multi-Modal Sensing and Shared Control: The Future of Wearable Robotics?

Meeting Room: 309 Multi-modal sensing and shared control are rapidly evolving fields that are pivotal to advancing the field of wearable robotics. This half-day workshop will delve into cutting-edge technologies that incorporate multiple sensing modalities—such as visual, tactile, and electrophysiological inputs—into wearable devices, improving their adaptability and functionality. We will also explore shared control, where […]

Learning Meets Model-Based Methods for Contact-Rich Manipulation

Meeting Room: 412 This workshop explores the challenge of enabling robots to autonomously handle complex, contact-rich interactions. Traditionally, model-based methods have provided structured frameworks for planning and control, while recent learning-based methods have leveraged large datasets to achieve new capabilities. However, these methods often overlook the structured insights that model-based approaches offer. As robotics continues […]