Robotics

This paper describes the development of algorithms that decide when to move, where to move, and how to look for people in a home environment. We introduce a design framework that defines the design principles, key decision points, and technical approaches for a social robot to proactively be with people for companionship and assistance in the home. Through a series of evaluations ranging from simulations

This paper introduces a deep learning (DL) approach to predicting congestion delays in large multi-robot systems. The problem is motivated by real-world problems in modern logistics automation, such as a warehouse with hundreds to thousands of coordinated mobile robots. Here, the large scale, the complexity of the control software, and the uncertainties of the robots’ dynamics make direct (simulated) prediction

Electro-Static Discharge (ESD) damage is a common failure mode in the manufacturing process. This paper introduces a continuous ESD monitoring system for New Product Introduction (NPI) line ESD qualification and early detection of potential static charges buildup on devices to defuse the ESD damage risk.

Household environments are visually diverse. Embodied agents performing Vision-and-Language Navigation (VLN) in the wild must be able to handle this diversity, while also following arbitrary language instructions. Recently, VisionLanguage models like CLIP have shown great performance on the task of zeroshot object recognition. In this work, we ask if these models are also capable of zero-shot language grounding

Task planning is an important component of traditional robotics systems enabling robots to compose fine grained skills to perform more complex tasks. Recent work building systems for translating natural language to executable actions for task completion in simulated embodied agents is focused on directly predicting low level action sequences that would be expected to be directly executable by a physical