DEFT: Dexterous Fine-Tuning for Real-World Hand Policies
This addresses data inefficiency in robotics for real-world dexterous manipulation, though it appears incremental as it builds on existing human priors and fine-tuning methods.
The paper tackles the problem of learning dexterous manipulation for real-world tasks, especially with soft, deformable objects and long-horizon challenges, by proposing DEFT, which leverages human-driven priors and online fine-tuning to achieve robust, data-efficient performance.
Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well as complex, relatively long-horizon tasks. However, learning such behaviors from scratch can be data inefficient. To circumvent this, we propose a novel approach, DEFT (DExterous Fine-Tuning for Hand Policies), that leverages human-driven priors, which are executed directly in the real world. In order to improve upon these priors, DEFT involves an efficient online optimization procedure. With the integration of human-based learning and online fine-tuning, coupled with a soft robotic hand, DEFT demonstrates success across various tasks, establishing a robust, data-efficient pathway toward general dexterous manipulation. Please see our website at https://dexterous-finetuning.github.io for video results.