HINT: Human-Intent Inception for Long-Horizon Robot Manipulation

2026-09-02

AI Summary

研究解决了机器人在复杂操作中理解人类意图的问题，通过HINT框架结合稀疏语义推理与持续视觉跟踪来实现高效任务执行。

Abstract

Humans can perform complex manipulations given a simple intent through an overall instruction, while continuously adapting to evolving visual observations. However, current vision-language action (VLA) models and other action policies struggle to realize this high-level intelligent behavior under dense, evolving visual inputs and sparse language guidance. Visual correlations can then dominate semantic intent, leading actions to follow visual shortcuts rather than human goals. We present HINT (Human-INTent INcepTion), an agentic framework inspired by the human manipulation principles: semantic intent changes sparsely at manipulation-pattern transitions, whereas continuous control primarily depends on the evolving object-hand relationship. HINT invokes semantic reasoning only at pattern transitions to resolve the current subtask and target, then maintains this commitment through multi-view grounding and visual tracking. We explore two visual interfaces-image-space semantic highlighting and attention-prior injection-to communicate the tracked intent to the action policy without introducing additional trainable parameters into the foundation action model. Experiments across three long-horizon tasks and out-of-distribution variants show that HINT substantially improves intent understanding, task progress, and end-to-end success across two foundation policies while preserving low-latency control.

Problem

• vision-language action models
• sparse language guidance
• visual correlations
• semantic intent

Innovation

• HINT
• semantic reasoning
• multi-view grounding
• visual tracking
• attention-prior injection