Guardians as You Fall: Active Mode Transition for Safe Falling

TL;DR

This paper introduces GYF, a novel framework enabling quadrupedal robots to actively tumble and recover to safe modes, significantly reducing impact forces during falls in dynamic scenarios.

Contribution

The paper presents the first active safe falling and recovery method for quadrupedal robots, enabling adaptive tumbling before irrecoverable poses.

Findings

GYF reduces maximum acceleration and jerk by 20-73% in simulations.

GYF effectively adapts to dynamic scenarios, improving safety during falls.

Experimental results confirm GYF's effectiveness in real-world tests.

Abstract

Recent advancements in optimal control and reinforcement learning have enabled quadrupedal robots to perform various agile locomotion tasks over diverse terrains. During these agile motions, ensuring the stability and resiliency of the robot is a primary concern to prevent catastrophic falls and mitigate potential damages. Previous methods primarily focus on recovery policies after the robot falls. There is no active safe falling solution to the best of our knowledge. In this paper, we proposed Guardians as You Fall (GYF), a safe falling/tumbling and recovery framework that can actively tumble and recover to stable modes to reduce damage in highly dynamic scenarios. The key idea of GYF is to adaptively traverse different stable modes via active tumbling before the robot shifts to irrecoverable poses. Via comprehensive simulation and real-world experiments, we show that GYF significantly reduces the maximum acceleration and jerk of the robot base compared to the baselines. In particular, GYF reduces the maximum acceleration and jerk by 20%~73% in different scenarios in simulation and real-world experiments. GYF offers a new perspective on safe falling and recovery in locomotion tasks, potentially enabling much more aggressive explorations of existing agile locomotion skills.