Acting in Meters: Learning Metric Interactions for Precise Robotic Manipulation

2026-09-16

AI Summary

本文提出了一种度量交互框架，通过显式建模对象和场景级别的物理空间交互来提高机器人操作精度，从而改进了视觉-语言-动作模型和世界-动作模型。

Abstract

Vision-Language-Action models and World-Action Models have advanced language-conditioned robotic manipulation, yet often leave metric relations among actions, objects, and scene geometry implicit. Human manipulation combines semantic understanding of task-relevant objects with spatial feedback that guides hand motion relative to objects and their surroundings. Inspired by this, we introduce a metric interaction framework that models object-level and scene-level interactions in physical Cartesian space at a shared metric scale. At the object level, Interaction-Centric Tokens (ICTs) explicitly represent end-effector pose trajectories relative to manipulated objects and are jointly denoised with actions, providing physically grounded interaction supervision. At the scene level, the Metric Action Interaction Field (MAIF) uses action and ICT queries to attend to metric scene point-cloud features and learns geometry-conditioned action corrections. Through two-stage adaptation, our framework improves diverse VLA and WAM baselines with a small number of additional parameters and training steps. Experiments demonstrate average success-rate gains of 0.80 and 3.59 percentage points on LIBERO and RoboTwin~2.0, respectively, alongside gains of 6.80 percentage points on real-world tasks and 7.45 percentage points on their out-of-distribution variants.

Problem

• Metric Interactions
• Robotic Manipulation
• Vision-Language-Action Models
• World-Action Models
• Spatial Feedback

Innovation

• Metric Interaction Framework
• Interaction-Centric Tokens (ICTs)
• Metric Action Interaction Field (MAIF)