ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
Score
78提出预训练+后训练两阶段框架与稳定训练配方，并首次在多个实体上结合视觉-触觉实现零样本迁移，信息增量显著。
方法设计系统，实验覆盖两款高自由度机器人及长时域任务，但论文细节和对比实验尚需更多公开验证。
为机器人灵巧操作提供可复用预训练策略，显著降低新任务学习成本，对工业机器人应用有较高实用价值。
论文编号为2608，时间为2026年8月，研究前沿，符合当前具身智能发展热点。
来自arXiv AI/CL/LG类别，作者单位未明示，但预印本通常严谨性较高，结果可复现性待后续验证。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper provides a complete framework with training recipe and hardware transfer validation, showing strong results and high reference value for the robot learning community.
AI commentary
「This work transfers the pre-training paradigm to dexterous manipulation. The most notable contribution is solving capability degradation during naive fine-tuning and achieving zero-shot sim-to-real transfer on multi-fingered hands with visuo-tactile inputs. For researchers in robot learning, RL algorithms, or embodied AI, ADEPT's stable post-training recipe and Geometric Fabric design offer direct reference value.」
Quick overview
ADEPT is a large-scale reinforcement learning framework designed to accelerate sim-to-real transferable dexterity across high degree-of-freedom robot embodiments via pre-training and post-training. It first pre-trains a dexterous policy on a generic object reposing task, then uses this pretrained behavior as a prior for post-training downstream policies, avoiding learning from scratch. The framework introduces a stable post-training recipe combining behavior-cloning distillation, critic warm-up, and conservative on-policy updates, along with a joint-space Geometric Fabric. It demonstrates zero-shot sim-to-real transfer on a 23-DoF Kuka-Allegro and a 29-DoF Flexiv-Sharpa, solving long-horizon tasks at human-level speed.
- Proposes ADEPT, a pre-training + post-training framework that accelerates dexterous manipulation learning on multi-fingered robots, avoiding re-learning basic skills for each new task.
- Introduces a stable post-training recipe (behavior-cloning distillation, critic warm-up, conservative on-policy updates) to mitigate capability degradation during transfer.
- Presents a joint-space Geometric Fabric that mediates between RL policy and robot, enabling safe and full exploitation of kinematic dexterity.
- Achieves zero-shot sim-to-real transfer on 23-DoF Kuka-Allegro and 29-DoF Flexiv-Sharpa, solving long-horizon tasks from challenging initial states at human-level speed.
Full text
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