Search Results for author: Sean Andrist

Found 7 papers, 2 papers with code

"Is This It?": Towards Ecologically Valid Benchmarks for Situated Collaboration

no code implementations30 Aug 2024 Dan Bohus, Sean Andrist, Yuwei Bao, Eric Horvitz, Ann Paradiso

We report initial work towards constructing ecologically valid benchmarks to assess the capabilities of large multimodal models for engaging in situated collaboration.

Embodied Question Answering Question Answering +1

SIGMA: An Open-Source Interactive System for Mixed-Reality Task Assistance Research

no code implementations16 May 2024 Dan Bohus, Sean Andrist, Nick Saw, Ann Paradiso, Ishani Chakraborty, Mahdi Rad

We introduce an open-source system called SIGMA (short for "Situated Interactive Guidance, Monitoring, and Assistance") as a platform for conducting research on task-assistive agents in mixed-reality scenarios.

Mixed Reality

Platform for Situated Intelligence

1 code implementation29 Mar 2021 Dan Bohus, Sean Andrist, Ashley Feniello, Nick Saw, Mihai Jalobeanu, Patrick Sweeney, Anne Loomis Thompson, Eric Horvitz

We introduce Platform for Situated Intelligence, an open-source framework created to support the rapid development and study of multimodal, integrative-AI systems.

Accelerating the Development of Multimodal, Integrative-AI Systems with Platform for Situated Intelligence

no code implementations12 Oct 2020 Sean Andrist, Dan Bohus

We describe Platform for Situated Intelligence, an open-source framework for multimodal, integrative-AI systems.

REFORM: Recognizing F-formations for Social Robots

1 code implementation17 Aug 2020 Hooman Hedayati, Annika Muehlbradt, Daniel J. Szafir, Sean Andrist

Recognizing and understanding conversational groups, or F-formations, is a critical task for situated agents designed to interact with humans.

Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations

no code implementations12 May 2019 Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz

We address the opportunity to maximize the utility of an overall computing system by employing reinforcement learning to guide the configuration of the set of interacting modules that comprise the system.

Decision Making reinforcement-learning +2

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