ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree-of-freedom (DoF) robot embodiments that can solve long-horizon tasks directly from raw visuo-tactile perception. ADEPT pretrains a dexterous policy on a generic object reposing task, then post-trains downstream policies with this pretrained behavior as a prior. ADEPT enables learning new behaviors that are otherwise difficult to discover from scratch on multi-fingered robots and avoids learning the same set of skill
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- PossiblePossibly related (embedding) · 52%Gradient-based Planning for World Models at Longer Horizons →
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- FuzzyOverlapping authors or contributors · 62%rasbt/LLMs-from-scratch →
“Shared author/contributor keys: yin”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “ADEPT: Accelerating Dexterity via Pre-Training and Post-Trai” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Jayjun Lee →
“ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Jessica Yin →
“ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Asif Rana →
“ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Nicholas Blauch →
“ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning”