TacEx represents each robot state using a fused latent state, an RGB visual embedding and an embedding of tactile force maps.
Touch-directed curiosity helps robots discover useful manipulation skills
TacEx steers reinforcement learning toward uncertain tactile interactions, producing contact-rich data for offline manipulation learning and vision-language-action model post-training.
Top University
Klemens Iten · Alexander Proshkin · Bhavya Sukhija · Stelian Coros · Andreas Krause · Pieter Abbeel · +1 more
ETH Zürich · University of California, Berkeley · The University of Texas at Austin
Research Digest··2 min read
Iten and colleagues introduce TacEx, an exploration framework that rewards robots for reducing uncertainty specifically about tactile outcomes rather than about every unpredictable transition.
Why this paper
From University of California, Berkeley and 2 others
In one line
Tactile-driven curiosity lets robots learn manipulation and grasp policies without task rewards or demonstrations, and improves post-training of VLA models.
What we could check
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