ForEnt: A Multi-Modal Dataset for Characterizing Quadruped Robot Entrapments in Forest Environments

2026-06-17

AI Summary

This work addresses the lack of specialized datasets for studying entanglement-induced failures in quadrupedal robots operating in forest environments, where vegetation such as vines can cause instability or even tip-over. To bridge this gap, the authors present ForEnt, a multimodal dataset collected using a low-cost Unitree Go2 platform across eight woodland sites near Southampton, UK. The dataset comprises 69 real-world entanglement events recorded over approximately 1.7 kilometers of traversed paths, with synchronized RGB-D images, LiDAR point clouds, proprioceptive sensor data, and third-person video footage, all meticulously annotated. ForEnt is the first dataset to specifically focus on entanglement failures in forested settings, establishing a reproducible benchmark for evaluating entanglement detection algorithms and advancing research on robotic robustness in complex natural terrains.

Abstract

Legged robots are increasingly deployed in forests for ecological surveying and monitoring, yet their autonomy is often interrupted consequent to the challenges posed in traversing forest environments. Forest entrapments, for example, when a robot's legs are ensnared in vines or other vegetation, result in loss of stability and toppling. Such events not only disrupt the mission and require manual intervention, but also risk damage to the robot hardware. To address the absence of a dedicated dataset to investigate these failure modes in forest environments, we present ForEnt, a multi-modal dataset collected with the low-cost Unitree Go2 quadruped across eight forest sites in the Southampton Common Woodlands, UK. For our dataset, over approximately 1.7 km of traversals in 11 sequences were conducted, yielding 69 recorded entrapment events. ForEnt includes time-synchronized RGB-D images, LiDAR scans, proprioceptive data, and third-person video, enabling analysis of terrain factors contributing to entrapment and providing labeled sensor streams for reproducible benchmarking. By supporting the evaluation of entrapment detection strategies, ForEnt lowers the barrier to developing robust quadruped robot deployments in challenging forest environments.

Problem

• quadruped robot
• forest entrapment
• autonomy failure
• terrain traversal
• robotic dataset

Innovation

• quadruped robot
• forest entrapment
• multi-modal dataset
• terrain-aware autonomy
• failure mode analysis