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RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
Ulas Berk Karli, Tesca Fitzgerald
Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for tasks where the policy performs po…
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…
INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models
Ulas Berk Karli, Ziyao Shangguan, Tesca FItzgerald
Recent Vision-Language-Action (VLA) models show strong generalization capabilities, yet they lack introspective mechanisms for anticipating failures and requesting help from a huma…
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…