FineCraft improves robot precision for detailed task handling
FineCraft: Enhancing VLA Models for Fine-Grained Manipulation
Robotics
Summary
Fine-grained manipulation involves robots understanding clear instructions, seeing very precise details, and carrying out careful actions. The authors created FineCraft, a system that improves existing models to better handle such detailed tasks by combining different policies and learning from expert examples. FineCraft can recognize specific parts of objects, adjust actions based on new observations, and plans as it works. This system performed well on a set of challenging tasks, beating previous references in those tests.
What this means in practice
- •For robotics engineers: Enable robots to handle tasks requiring precise detection and manipulation of small object parts during complex multi-step tasks.
- •For industrial automation teams: Improve robotic assembly lines by integrating systems that adapt policies based on real-time observations and fine visual details.
Authors
Shuaike Zhang, Ziheng Quan, Junhao Ran, Muhe Ding, Shaokun Wang, Jianlong Wu
Abstract
Fine-grained manipulation couples instruction grounding, local visual precision, and sustained execution. We present FineCraft, a framework that adapts pretrained models for fine-grained manipulation in the MetaFine Challenge. A task-routed mixture of policies selects independently adapted DINOv2-DiT or pretrained $π_{0.5}$ policies through a common observation-action interface. Local visual features help identify the instructed object parts, demonstrations and expert continuations teach the required actions, and new observations guide replanning during execution. FineCraft ranks fourth with an overall score of $0.878$, exceeding the published reference on all five tasks. Official results and local diagnostics characterize performance across visual perturbations, instruction changes, and multi-stage manipulation. Code is available at https://github.com/eqcy/FineCraft