QUAR-VLA: Vision-Language-Action Model for Quadruped Robots

TL;DR

This paper introduces QUAR-VLA, a vision-language-action framework for quadruped robots that integrates perception, planning, and decision-making to improve autonomous interaction and task execution.

Contribution

It presents a novel integrated paradigm and a transformer-based model, QUART, along with a large-scale dataset, QUARD, for training and evaluating vision-language-action tasks in quadruped robots.

Findings

Achieved high performance in diverse robotic tasks

Enabled emergent capabilities in quadruped robots

Validated approach with 4000 evaluation trials

Abstract

The important manifestation of robot intelligence is the ability to naturally interact and autonomously make decisions. Traditional approaches to robot control often compartmentalize perception, planning, and decision-making, simplifying system design but limiting the synergy between different information streams. This compartmentalization poses challenges in achieving seamless autonomous reasoning, decision-making, and action execution. To address these limitations, a novel paradigm, named Vision-Language-Action tasks for QUAdruped Robots (QUAR-VLA), has been introduced in this paper. This approach tightly integrates visual information and instructions to generate executable actions, effectively merging perception, planning, and decision-making. The central idea is to elevate the overall intelligence of the robot. Within this framework, a notable challenge lies in aligning fine-grained instructions with visual perception information. This emphasizes the complexity involved in ensuring that the robot accurately interprets and acts upon detailed instructions in harmony with its visual observations. Consequently, we propose QUAdruped Robotic Transformer (QUART), a family of VLA models to integrate visual information and instructions from diverse modalities as input and generates executable actions for real-world robots and present QUAdruped Robot Dataset (QUARD), a large-scale multi-task dataset including navigation, complex terrain locomotion, and whole-body manipulation tasks for training QUART models. Our extensive evaluation (4000 evaluation trials) shows that our approach leads to performant robotic policies and enables QUART to obtain a range of emergent capabilities.