Event-grounded SAE analysis in VLA policies produces stronger causal effects on robot behavior than standard methods by anchoring features to clustered end-effector keyframes across simulations and real-robot tests.
The utility of explainable ai in ad hoc human-machine teaming.Advances in neural information processing systems, 34:610–623, 2021
2 Pith papers cite this work. Polarity classification is still indexing.
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IBTS framework uses influence shaping to improve zero-shot human-machine teaming beyond partner diversity alone, with gains shown in Overcooked-AI simulations and a 30-subject human study.
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Event-Grounded Sparse Autoencoders for Vision-Language-Action Policies
Event-grounded SAE analysis in VLA policies produces stronger causal effects on robot behavior than standard methods by anchoring features to clustered end-effector keyframes across simulations and real-robot tests.
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Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming
IBTS framework uses influence shaping to improve zero-shot human-machine teaming beyond partner diversity alone, with gains shown in Overcooked-AI simulations and a 30-subject human study.