117 citations · 488 across the 32 of their papers we have counts for
6 papers · 1 filter
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon +5
We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to seque…
Vision-and-Dialog Navigation
Jesse Thomason, Michael Murray, Maya Cakmak +1
Robots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Visio…
Augmenting Knowledge through Statistical, Goal-oriented Human-Robot Dialog
Saeid Amiri, Sujay Bajracharya, Cihangir Goktolga +2
Some robots can interact with humans using natural language, and identify service requests through human-robot dialog. However, few robots are able to improve their language capabi…
Interpreting Black Box Models via Hypothesis Testing
Collin Burns, Jesse Thomason, Wesley Tansey
In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries…
Prospection: Interpretable Plans From Language By Predicting the Future
Chris Paxton, Yonatan Bisk, Jesse Thomason +2
High-level human instructions often correspond to behaviors with multiple implicit steps. In order for robots to be useful in the real world, they must be able to to reason over bo…
Improving Grounded Natural Language Understanding through Human-Robot Dialog
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov +6
Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an enviro…