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

Mingyu Mei 1
Haojie Xu 1
Shihao Jin 1
Zibo Dai 1
Qihao Cheng 1
Zhengrui Lv 1
Hongjie Fang 2
Shirun Tang 3
Guang Chen 1,4
Xinyue Zhao 1
Huiliang Shen 1
Zaixing He 1,†
1Zhejiang University, 2Shanghai Jiao Tong University, 3Noematrix, 4EndlessAI
†Corresponding author
Paper arXiv Coming soon

Overview

Abstract

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

Motivation and overview of HINT.
HINT turns sparse language intent into continuous visual guidance. (a) Humans identify, track, and attend to the intended target. (b) HINT reasons at pattern transitions, tracks the target, and injects spatial intent into the action policy. (c) Evaluation covers unseen objects, layouts, colors, and instructions.

Method

System overview of HINT.
System overview. (1) A pattern router selects views and the next semantic-update time. (2) At an update, HINT resolves and grounds the active target, then tracks it. (3) Intent is injected via pixel highlighting and a token-level attention prior.

HINT separates what to act on from how to act: it maintains human intent, while the pretrained action policy retains responsibility for motor execution.

  • Which & when. Pattern-aware perception scheduling uses the current manipulation pattern to determine which view to prioritize and when semantic perception should be updated. Semantic reasoning is invoked only at pattern transitions, coupling sparse intent updates with continuous visuomotor control.
  • What & where. High-level semantic intent changes sparsely, whereas low-level visual attention evolves continuously. Pattern-triggered reasoning establishes a semantic commitment when the manipulation context changes; goal-consistent tracking then maintains the spatial realization of the committed target.
  • How. View-routed semantic intent injection decouples semantic reasoning from action generation. Complementary image-level highlighting and token-level attention bias communicate the tracked target to the policy without adding trainable parameters to the foundation action model.

Experiments

We evaluate on dual-arm PiPER robots with one global and two wrist cameras, using fruit-vegetable sorting, word spelling, and peg-in-hole insertion. HINT is a plug-and-play interface on Wall-OSS-0.5 and π0.5\pi_{0.5}, with no extra trainable parameters in the action backbone.

Evaluation tasks and their manipulation-pattern decomposition.
Tasks. Sorting and spelling loop over multiple pick-and-place subtasks S1→⋯→SKS_1 \to \cdots \to S_K (free move → pre-contact → transport). Peg-in-hole consists of two stages S1S_1 and S2S_2, each further requiring dexterous contact.

In-distribution

IS

+39.0 pp

Intention Score vs π0.5

52.2% → 91.2%

Sub. SR

+39.0 pp

Subtask Success Rate vs π0.5

52.2% → 91.2%

Full SR

+50.0 pp

Full-Task Success Rate vs π0.5

10.0% → 60.0%

Fruit-Vegetable Sorting Place fruits and vegetables into the baskets specified by color.

In-distribution
Wall-OSS-0.5 π₀.₅ Wall-OSS-0.5 + HINT π₀.₅ + HINT

Fruit-Vegetable Sorting: IS

100%75%50%25%0%
53.1
52.2
85.8
91.2
Wall-OSS-0.5 π₀.₅ Wall + HINT π₀.₅ + HINT

Word Spelling: IS

100%75%50%25%0%
44.7
44.7
83.0
97.9
Wall-OSS-0.5 π₀.₅ Wall + HINT π₀.₅ + HINT

Peg-in-Hole Insertion: IS

100%75%50%25%0%
65.0
35.0
86.7
100.0
Wall-OSS-0.5 π₀.₅ Wall + HINT π₀.₅ + HINT

Out-of-distribution

Semantic-compositional OOD configurations.
OOD settings. Unseen objects, attributes, layouts, goals, and instructions; required motor primitives stay familiar.

IS

+26.1 pp

Intention Score vs π0.5

56.5% → 82.6%

Sub. SR

+28.2 pp

Subtask Success Rate vs π0.5

52.2% → 80.4%

Full SR

+30.0 pp

Full-Task Success Rate vs π0.5

0.0% → 30.0%

Fruit-Vegetable Sorting Place fruits and vegetables into the bowls specified by spatial relations.

Out-of-distribution
Wall-OSS-0.5 π₀.₅ Wall-OSS-0.5 + HINT π₀.₅ + HINT

Fruit-Vegetable Sorting: IS

100%75%50%25%0%
47.8
56.5
73.9
82.6
Wall-OSS-0.5 π₀.₅ Wall + HINT π₀.₅ + HINT

Word Spelling: IS

100%75%50%25%0%
36.4
40.9
81.8
95.5
Wall-OSS-0.5 π₀.₅ Wall + HINT π₀.₅ + HINT

Peg-in-Hole Insertion: IS

100%75%50%25%0%
50.0
26.7
83.3
90.0
Wall-OSS-0.5 π₀.₅ Wall + HINT π₀.₅ + HINT

Highlighting Inputs

These videos show Policy observations after pixel-level visual semantic highlighting. Training observations are rendered with the same overlay used at deployment. Each manipulation pattern determines a unique routed view—global or wrist—for highlighting; all other views remain unmodified.

Fruit-Vegetable Sorting

Highlighting

Demo 1 Put vegetables in the pink basket and fruits in the blue basket.

HINT Demo 1

Demo 2 Put fruits in the pink basket and vegetables in the blue basket.

HINT Demo 2

BibTeX

@misc{mei2026hinthumanintentinceptionlonghorizon,
      title={HINT: Human-Intent Inception for Long-Horizon Robot Manipulation},
      author={Mingyu Mei and Haojie Xu and Shihao Jin and Zibo Dai and Qihao Cheng and Zhengrui Lv and Hongjie Fang and Shirun Tang and Guang Chen and Xinyue Zhao and Huiliang Shen and Zaixing He},
      year={2026},
      eprint={2609.02653},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2609.02653},
}