发表机构
Unitree Robotics（宇树科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出行为可操控性概念及首个基准RoboSteer，通过三层级框架和统一评估，对9个行为基础模型进行大规模实证研究，旨在推动意图实现作为通用具身智能的基础。
AI 中文摘要
行为基础模型（BFMs）正逐渐成为将人类意图转化为可执行的人形行为的范式。随着这些模型从行为生成向通用行为系统演进，一个基本问题随之浮现：它们能否根据用户意图被可靠地操控？在本文中，我们引入了行为可操控性的概念，将其定义为BFMs忠实生成满足用户指定意图的行为的能力。为研究这一能力，我们提出了RoboSteer，这是首个针对BFMs行为可操控性的基准。RoboSteer将行为可操控性组织为三个层级——条件操控、约束操控和组合操控——并建立了一个由大规模多模态运动语料库支持的统一评估框架。利用RoboSteer，我们对9个现有BFMs进行了首次大规模行为可操控性实证研究。我们认为行为可操控性不仅仅是一种控制运动的能力：它关乎具身系统如何将人类意图转化为有目的的行动。我们希望RoboSteer能推动意图实现研究，作为通用具身智能的基础。
英文摘要
Behavior Foundation Models (BFMs) are emerging as a paradigm for translating human intentions into executable humanoid behaviors. As these models evolve beyond behavior generation toward general-purpose behavioral systems, a fundamental question arises: can they be reliably steered according to user intentions? In this paper, we introduce the concept of behavioral steerability, defined as the ability of BFMs to faithfully generate behaviors that satisfy user-specified intentions. To study this capability, we present RoboSteer, the first benchmark for behavioral steerability in BFMs. RoboSteer organizes behavioral steerability into a three-level hierarchy-Conditional Steering, Constraint Steering, and Compositional Steering-and establishes a unified evaluation framework supported by a large-scale multimodal motion corpus. Using RoboSteer, we conduct the first large-scale empirical study of behavioral steerability across 9 existing BFMs. We view behavioral steerability as more than a capability for controlling motion: it concerns how embodied systems translate human intentions into purposeful actions. We hope RoboSteer will advance research on intention realization as a foundation for general-purpose embodied intelligence.
