# Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots

## TL;DR

This paper develops a simulation framework using deep reinforcement learning to enable quadruped robots with active spines to achieve faster, more efficient bounding locomotion, demonstrating significant performance improvements.

## Contribution

It introduces a novel simulation and learning framework for active spine control in quadruped robots, enhancing bounding speed and efficiency.

## Key findings

- Achieved 2.1 m/s bounding speed in simulation
- Active spine increased stride length and reduced cost of transport
- Improved natural frequency to realistic values

## Abstract

In this work, we provide a simulation framework to perform systematic studies on the effects of spinal joint compliance and actuation on bounding performance of a 16-DOF quadruped spined robot Stoch 2. Fast quadrupedal locomotion with active spine is an extremely hard problem, and involves a complex coordination between the various degrees of freedom. Therefore, past attempts at addressing this problem have not seen much success. Deep-Reinforcement Learning seems to be a promising approach, after its recent success in a variety of robot platforms, and the goal of this paper is to use this approach to realize the aforementioned behaviors. With this learning framework, the robot reached a bounding speed of 2.1 m/s with a maximum Froude number of 2. Simulation results also show that use of active spine, indeed, increased the stride length, improved the cost of transport, and also reduced the natural frequency to more realistic values.