4 papers
Enhancing Goal Inference via Correction Timing
Anjiabei Wang, Shuangge Wang, Tesca Fitzgerald
Corrections offer a natural modality for people to provide feedback to a robot, by (i) intervening in the robot's behavior when they believe the robot is failing (or will fail) the…
TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization
Yuxuan Ding, Shuangge Wang, Tesca Fitzgerald
Robots often struggle to generalize from a single demonstration due to the lack of a transferable and interpretable spatial representation. In this work, we introduce TReF-6, a met…
Effects of Robot Competency and Motion Legibility on Human Correction Feedback
Shuangge Wang, Anjiabei Wang, Sofiya Goncharova +2
As robot deployments become more commonplace, people are likely to take on the role of supervising robots (i.e., correcting their mistakes) rather than directly teaching them. Prio…
TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models
Ziyao Shangguan, Chuhan Li, Yuxuan Ding +4
Existing benchmarks often highlight the remarkable performance achieved by state-of-the-art Multimodal Foundation Models (MFMs) in leveraging temporal context for video understandi…