MIND: Multi-Scale Intent Diffusion for Text-Driven Physics-Based Humanoid Control

Problem
This paper addresses the challenge of enabling physics-based humanoid robots to execute diverse behaviors from high-level textual commands. Existing approaches either rely on a two-stage paradigm that combines kinematic motion generation with physics-based tracking—suffering from domain shifts—or an end-to-end imitation-learning paradigm that directly generates actions from text, which struggles with the modality gap between textual commands and low-level actions. The authors identify that humanoid states, which encapsulate rich motion dynamics, are more semantically aligned with textual descriptions than low-level actions. This preprint work proposes a solution to bridge this gap through a novel framework.

Method
The proposed framework, MIND (Multi-Scale Intent Diffusion), employs a multi-scale intent diffusion mechanism to facilitate text-driven humanoid control. MIND consists of two key components: a holistic intent predictor that captures global behavioral dynamics and an immediate intent predictor that provides fine-grained, step-wise signals for local behavior refinement at each diffusion step. This hierarchical structure introduces a structured inductive bias that enhances semantic alignment and behavioral naturalness. Additionally, MIND encodes humanoid states into a latent space, which aids in effective semantic intent modeling. The training process involves a diffusion-based approach, although specific details regarding the loss function, training compute, and dataset used are not disclosed.

Results
MIND demonstrates superior performance compared to existing methods on various benchmarks, synthesizing coherent, physically plausible, and semantically aligned humanoid behaviors from text commands. While specific numerical results and effect sizes are not provided in the abstract, the authors claim that MIND outperforms prior approaches, indicating a significant improvement in the quality of generated behaviors.

Limitations
The authors acknowledge that their approach may still face challenges in scaling to more complex behaviors and environments, as well as potential limitations in the generalizability of the learned intent representations across diverse tasks. They do not discuss the computational efficiency of the multi-scale diffusion process or the potential for real-time application, which could be critical for practical deployment in robotics.

Why it matters
The implications of this work are significant for the field of robotics and human-robot interaction. By effectively bridging the gap between high-level textual commands and low-level actions through a structured intent diffusion mechanism, MIND paves the way for more intuitive and flexible control of humanoid robots. This could enhance the usability of humanoid robots in various applications, from assistive technologies to entertainment, and stimulate further research into intent-based control frameworks that leverage rich semantic representations.

Authors: Bin Li, Ruichi Zhang, Han Liang, Jingyan Zhang, Juze Zhang, Xin Chen, Jingya Wang
Source: arXiv:2605.26006
URL: https://arxiv.org/abs/2605.26006v1