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Notes on robotic manipulation
3 notes
Freeform Preference Learning for Robotic Manipulation
Researchers introduce Freeform Preference Learning (FPL), a method for training robotic policies based on natural-language preferences provided by humans, rather than binary labels. The method uses the preferences to…
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
The researchers introduced a new framework called E-TTS for robotic manipulation that combines reasoning and action scaling using historical context. Unlike traditional open-loop TTS methods, E-TTS includes feedback…
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
Researchers have developed a new framework called E-TTS for robotic manipulation tasks. This framework combines reasoning and action scaling to address two challenges: the lack of study on the scaling mechanism for…
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