---
title: "Intention Distillation: Enhancing Vision-Language-Action Models with Semantic Objective Understanding"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.23478v1"
permalink: "https://feed.astralor.com/items/itm_6c9da22dfef8ac27b572eb97"
published: "2026-08-24T16:42:49.000Z"
score: 75
channel: "ai"
tags: ["GR00T N1.7","VLA Models","π0.5","Embodied AI","Intention Distillation","Robot Learning"]
---

# Intention Distillation: Enhancing Vision-Language-Action Models with Semantic Objective Understanding

## AI commentary

> This paper tackles an overlooked issue in training VLA models—behavior cloning learns actions but not the underlying intent. INDI is clever because it uses a teacher VLM to distill the goal directly into the decoder without requiring extra future-information modules, keeping deployment costs low. It is especially valuable for teams working on robotic manipulation and embodied AI, as it significantly narrows the semantic gap in long-horizon tasks.

## At a glance

Vision-Language-Action (VLA) models typically train action decoders via behavior cloning, which overlooks the semantic objective behind demonstrated behavior. This paper proposes Intention Distillation (INDI), which uses a frozen teacher VLM during training to distill behavioral intent into the action decoder. At deployment, the VLA recovers a multimodal intent representation at an intermediate decoder layer and uses it to organize action prediction alongside representations of behavioral progress and outcomes. On SimplerEnv-Bridge, INDI improves GR00T-N1.7 from 64.3% to 84.7%; on RoboCasa Kitchen, it raises the controlled GR00T-N1.7 baseline from 64.1% to 70.3%, with consistent gains on π0.5 across both benchmarks. In real-world tasks, average success improves from 62.0% to 68.7%, with up to 12.0 percentage point gains on longer-horizon tasks. Analyses show the recovered latent is used by the decoder, captures behavior objective and execution progress, and organizes downstream predictions in an objective-dependent manner.

## Key points

- Proposes INDI, which distills behavior-level intent into the action decoder via a teacher VLM, explicitly modeling semantic behavior objectives.
- Significantly improves GR00T-N1.7 and π0.5 success rates on multiple simulation and real-world benchmarks, with larger gains on longer-horizon tasks.
- Experiments show the decoder uses the recovered intent representation to capture behavior objective and execution progress, and organizes action predictions in an objective-dependent manner.

## Full text

Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cloning. This supervises which motor command was demonstrated while leaving implicit the local objective served by the behavior under the instruction. Future-based supervision enriches action learning with frames, latent observations, trajectories, or motion representations, but these signals capture particular realizations of what may happen rather than the shared semantic objective of the forthcoming behavior. We propose Intention Distillation (INDI), which distills behavior-level intent into the action decoder. During training, a frozen teacher VLM interprets a demonstrated segment from the current observation, instruction, coarse action summary, and corresponding execution video. From its standard inputs, the deployed VLA recovers the resulting multimodal intent representation at an intermediate decoder layer and uses it to organize action prediction together with representations of how the behavior unfolds and what it achieves. On SimplerEnv-Bridge, INDI improves GR00T-N1.7 from 64.3% to 84.7%, and on RoboCasa Kitchen it improves the controlled GR00T-N1.7 baseline from 64.1% to 70.3%, with consistent gains on $π_{0.5}$ across both benchmarks. In real-world tasks, INDI improves average success from 62.0% to 68.7%, with gains of up to 12.0 pp on longer-horizon tasks. Further analyses show that the recovered latent is used by the decoder, captures behavior objective and execution progress, and organizes downstream predictions in an objective-dependent manner. These results show that action decoders benefit from explicitly modeling the semantic objective of the behavior they generate.

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