UEREBot: Learning Safe Quadrupedal Locomotion under Unstructured Environments and High-Speed Dynamic Obstacles

2026-02-07

AI Summary

This work addresses the challenge of enabling quadrupedal robots to simultaneously achieve long-horizon goal navigation, traverse complex static terrains, and avoid high-speed dynamic obstacles in unstructured environments. To this end, the authors propose UEREBot, a novel hierarchical framework that introduces layered decision-making into high-speed dynamic obstacle avoidance for quadrupedal locomotion. The upper layer employs a spatiotemporal planner to generate goal-directed trajectories and threat signals, while the lower layer integrates navigation and reflexive actions through a threat-aware switching mechanism, with safety guarantees provided by control barrier functions (CBFs). Experiments in Isaac Lab simulations and on the Unitree Go2 platform demonstrate that the proposed approach significantly improves both obstacle avoidance success rates and locomotion stability in scenarios involving intricate static structures and fast-moving dynamic obstacles, all while maintaining efficient progress toward the goal.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Quadruped robots are increasingly deployed in unstructured environments. Safe locomotion in these settings requires long-horizon goal progress, passability over uneven terrain and static constraints, and collision avoidance against high-speed dynamic obstacles. A single system cannot fully satisfy all three objectives simultaneously: planning-based decisions can be too slow, while purely reactive decisions can sacrifice goal progress and passability. To resolve this conflict, we propose UEREBot (Unstructured-Environment Reflexive Evasion Robot), a hierarchical framework that separates slow planning from instantaneous reflexive evasion and coordinates them during execution. UEREBot formulates the task as a constrained optimal control problem blueprint. It adopts a spatial--temporal planner that provides reference guidance toward the goal and threat signals. It then uses a threat-aware handoff to fuse navigation and reflex actions into a nominal command, and a control barrier function shield as a final execution safeguard. We evaluate UEREBot in Isaac Lab simulation and deploy it on a Unitree Go2 quadruped equipped with onboard perception. Across diverse environments with complex static structure and high-speed dynamic obstacles, UEREBot achieves higher avoidance success and more stable locomotion while maintaining goal progress than representative baselines, demonstrating improved safety--progress trade-offs.

Problem

• quadrupedal locomotion
• unstructured environments
• dynamic obstacles
• collision avoidance
• goal progress

Innovation

• hierarchical control
• reflexive evasion
• control barrier function
• quadrupedal locomotion
• dynamic obstacle avoidance