Public articles linked to the same research event.
arXiv The work introduces TacEx, a framework that decomposes model uncertainty across sensory modalities and directs curiosity toward the tactile channel, enabling a robot to learn manipulation and grasping during exploration without task rewards or expert demonstrations; the resulting interaction-dense dataset supports offline learning of downstream pick-and-place policies, and tactile-driven exploration is further used to post-train vision-language-action (VLA) models, substantially improving downstream performance while remaining highly sample-efficient.
The work introduces TacEx, a framework that decomposes model uncertainty across sensory modalities and directs curiosity toward the tactile channel, enabling a robot to learn manipulation and grasping during exploration without task rewards or expert demonstrations; the resulting interaction-dense dataset supports offline learning of downstream pick-and-place policies, and tactile-driven exploration is further used to post-train vision-language-action (VLA) models, substantially improving downstream performance while remaining highly sample-efficient.
The work introduces TacEx, a framework that decomposes model uncertainty across sensory modalities and directs curiosity toward the tactile channel, enabling a robot to learn manipulation and grasping during exploration without task rewards or expert demonstrations; the resulting interaction-dense dataset supports offline learning of downstream pick-and-place policies, and tactile-driven exploration is further used to post-train vision-language-action (VLA) models, substantially improving downstream performance while remaining highly sample-efficient.
The work introduces TacEx, a framework that decomposes model uncertainty across sensory modalities and directs curiosity toward the tactile channel, enabling a robot to learn manipulation and grasping during exploration without task rewards or expert demonstrations; the resulting interaction-dense dataset supports offline learning of downstream pick-and-place policies, and tactile-driven exploration is further used to post-train vision-language-action (VLA) models, substantially improving downstream performance while remaining highly sample-efficient.