RoboCoach: World Models as Active Coaches for Compositional Robot Skills

arXiv 2026 38.1 method, system, application

TLDR

A world-model-guided coaching framework uses imagined failures in a shared action-conditioned world model to target demonstrations and improve compositional robot skills.

评分理由

The paper presents a clear framework with strong empirical support across simulations and real robots, including success correlations and controlled comparisons. Weaknesses are limited visibility into world model training details and potential limitations of the coaching approach from the abstract alone.

Read-first 评分解释

综合优先阅读分 38.1，由主题、引用、图谱、方法、可复现性和近期性等信号加权得到。原始总分保留为 45。

近期性 5%

100

使用温和的时间衰减，让近期论文更容易浮现，同时保留较早基础工作的价值。年份：2026

方法质量 15%

检查可见的摘要与分析字段，寻找实验、数据集、基线、指标和局限性等方法证据。命中信号：基线、指标、结果

主题相关性 25%

64.3

使用现有 LLM 关键词相关性评分，并归一化到 0-100。关键词：world model、world simulator、generative world model、interactive world model、video world model、world dynamics prediction、model-based reinforcement learning world model

可复现性 15%

检查链接和可见文本中的论文、代码、数据集、工件与仓库信号。论文：有；代码：无；数据：无；命中信号：GitHub

图谱声望 15%

5.7

基于可用 OpenAlex 引用关系构建局部引用图，并计算 PageRank 风格的图谱声望。局部 PageRank：0.0016

引用影响力 15%

使用 OpenAlex 形态的引用元数据作为文献关注度信号，并与论文本身质量分开处理。引用数：0

引用速度 10%

引用速度按发表年限估算年均引用，降低旧论文天然占优的偏差。年均引用：0.00

研究版图角色

前沿论文方法锚点

排序敏感性

稳定性：volatile；排名波动范围：395。

关键词评分

world model

world dynamics prediction

world simulator

interactive world model

generative world model

model-based reinforcement learning world model

video world model

深度分析

创新点

ROBOCOACH: a world-model-guided coaching framework that uses imagined failures to decide what to teach next and where to apply supervision.
Route-Imagine-Diagnose-Improve (RIDI) loop: executes reusable skill experts inside COACHWORLD, a shared action-conditioned world model, and uses a progress judge to record the first subtask that fails to complete.
Aggregated imagined-failure records select which subtask demonstrations to acquire and which expert adapters to update.
Uses world models as active coaches for modular, compositional policy improvement, avoiding costly end-to-end demonstrations.

方法

ROBOCOACH uses a shared action-conditioned world model called COACHWORLD within a Route-Imagine-Diagnose-Improve loop: it routes imagined rollouts through reusable skill experts, diagnoses the first failing subtask with a progress judge, and aggregates failure records to select subtask demonstrations and expert adapter updates. The method is evaluated across two simulation suites and two real-robot platforms, with controlled comparisons against matched baselines under matched data budgets and update schedules. Metrics include imagined-versus-deployed success correlation and task success rates.

关键结果

Controlled comparisons show ROBOCOACH outperforms matched baselines under matched data budgets and update schedules, and imagined and deployed success correlate at rho = 0.840 over 22 task-policy pairs. With only 150 additional subtask demonstrations, success rises from 13.3% to 75.0% on Franka and from 40.0% to 83.8% on AgileX, while coached experts transfer to four held-out compositions with 35.0% average success versus 0% for a shared-policy baseline.

资源

论文