バリア認証安全アーキテクチャによる脚式ロボットの視覚言語ナビゲーションのケージング
Caging Vision-Language Navigation for Legged Robots via Barrier-Certified Safety Architecture
VLAプランナを低層の物理接地層とSDFベースの衝突回避保証付きMPPIプランナで補強し、人型・四脚ロボットの実機で安全な視覚言語ナビゲーションを実現した。
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著者: Abhijeet M. Kulkarni, Saimouli Katragadda, Yuze Du, Zijun Sha, Guoquan Huang
分類: cs.RO, eess.SY
原文アブストラクト
By harnessing the inherent commonsense capabilities of pretrained Large Language Models (LLMs) and Vision-Language Models (VLMs), current Vision-Language Action(VLA) models for legged robots excel at interpreting language-based navigation commands but lack the physical grounding and explicit safety assurance required for real-world operation. Therefore, this paper aims to cage Legged-Vision-Language Navigation (VLN) through a safety architecture that provides physical grounding and ensures navigation safety. The key idea is not to abandon the powerful capabilities of end-to-end VLA planning, but rather to augment and cage them. In particular, we integrate a high-level VLA planner with a low-level physical-grounding layer that leverages efficient state estimation, in-situ 3D spatial perception, and an Signed Distance Field (SDF) representation to generate executable trajectories. We provide a navigation planner with provable collision-avoidance guarantees under the stated assumptions by combining a low-dimensional Bezier-parameterized MPPI planner with Discrete Exponential Barrier Function (dEBF) constraints. The proposed Legged-VLN system is modular, making it compatible with various VLM/VLA models and agnostic to the robot platform. The proposed system is validated with extensive real-world experiments on humanoid and quadrupedal robots, demonstrating versatile, efficient, and safe vision-language driven navigation capabilities.