Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum

TL;DR

This paper introduces a novel humanoid manipulation framework utilizing large models for active spatial perception and generalizable action generation, addressing challenges in complex 3D environments.

Contribution

It presents a new framework with Active Spatial Brain and Action Cerebellum components that improve spatial understanding and action generalization without extensive real-robot data.

Findings

Strong performance in spatial perception and understanding tasks.

Effective real-robot task execution across diverse environments.

Benchmark results demonstrate robustness and generalization.

Abstract

In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this task poses two key challenges: 1) Spatial understanding is challenging in complex 3D environments with diverse spatial relations. 2) Action generation is difficult to generalize, as limited and costly real-robot data restricts data-driven models generalization. To address these challenges, we propose a generalizable humanoid loco-manipulation framework that leverages the spatial perception and action generation capabilities of multi-agent large models. Specifically, our framework includes two components: Active Spatial Brain for active spatial perception and decision-making, and Generalizable Action Cerebellum for executable robot action generation. The first component actively perceives the spatial scene and makes decisions on task planning and subtask decomposition. The second component generate executable robot actions based on the decisions made by the first module without needs of task-specific real robot data. To benchmark our framework, we design a set of spatial manipulation tasks from two perspectives: evaluating spatial perception and understanding, and assessing real-robot task performance. The results demonstrate strong performance on both aspects across diverse tasks and environments.