DOI: 10.5061/dryad.vmcvdnd48
title: High-speed control and navigation for quadrupedal robots on complex and discrete terrain

description: High-speed legged navigation in discrete and geometrically complex
 environments is a highly challenging task due to the
 high-degree-of-freedom dynamics and long-horizon, non-convex nature of the
 optimization problem. In this work, we propose a hierarchical navigation
 pipeline for legged robots that is capable of traversing such environments
 at high speed. The proposed pipeline consists of a planner and tracker
 module. The planner module finds physically feasible foothold plans by
 sampling-based optimization strategy, which involves sequential filtering.
 This filtering process utilizes multiple criteria, including simple
 heuristics and a learned neural network, to quickly eliminate bad samples.
 Subsequently, rollouts are performed in a physics simulation to identify
 the best foothold plan concerning the engineered cost function and to
 confirm their physical consistency. This hierarchical planning module is
 computationally efficient and physically accurate at the same time. The
 tracker aims to accurately step on the target footholds from the planning
 module. During the training stage, the foothold target distribution is
 given by a generative model which is trained adversarially with the
 tracker. This process ensures that the tracker is trained in a
 sufficiently difficult environment. The resulting tracker is capable of
 overcoming terrains that are more difficult than what the previous methods
 could manage. We demonstrate this using Raibo, our in-house dynamic
 quadrupedal robot. The results are highly dynamic and agile motions: Raibo
 is capable of running on vertical walls, jumping a 1.3m gap, running over
 stepping stones at 4 m/s, and autonomously navigating on terrains full of
 30-degree ramps, stairs, and boxes of various sizes.
description: # High-speed control and navigation for quadrupedal robots on complex and
 discrete terrain Dataset DOI:
 [10.5061/dryad.vmcvdnd48](10.5061/dryad.vmcvdnd48) This dataset includes
 both experimental data and Python plotting scripts used to reproduce
 specific figures from the paper *"High-speed control and navigation
 for quadrupedal robots on complex and discrete terrain."* ### Data
 Files * **Doublewall.csv** Contains log data recorded from real-world
 experiments conducted in the "Doublewall" scenario. This file
 includes time-series data relevant to the robot's locomotion and the
 robot's state on this terrain. Here, time is written in microsecond
 unit. Vx represents the robot's speed on x coordinate of robot's
 axes and written in m/s unit. Actual torque 2,8 represent the torque of
 front left knee and rear left knee joint and written in N*m unit.
 genCoordinate 13,16 represent the joint angle of rear left roll and rear
 right roll joint and written in radian unit. * **Stepscape_right.csv**
 Contains experimental logs collected from the "Stepscape"
 scenario. Similar to `Doublewall.csv`, this file logs physical data of the
 quadrupedal robot. Here, time is written in microsecond unit. Vx
 represents the robot's speed on x coordinate of robot's axes and
 written in m/s unit. Actual torque 2,8 represent the torque of front left
 knee and rear left knee joint and written in N*m unit. genCoordinate 13,16
 represent the joint angle of rear left roll and rear right roll joint and
 written in radian unit. * **Tracking_error.csv** This file records the
 foothold tracking errors for both real and simulated experiments in each
 scenario. These errors are used to generate **Figure 3d** of the paper,
 showing the difference between foothold plan and actual foothold positions
 ( unit: meter). ### Python Scripts * **fig_3c.py** * **fig_3d.py** *
 **fig_4a.py** * **fig_4b.py** These scripts are used to reproduce
 **Figures 3c, 3d, 4a, and 4b** from the publication. They load the
 relevant CSV data, process it, and generate visualizations accordingly.
 **Figure 3c)** Success rates across various map height errors in
 simulation **Figure 3d)** Tracking error measured in simulation and the
 real world across 12 scenarios in Fig. 1 (sample size N=10). Each colored
 bar represents the interval corresponding to the mean ± SD. The black
 circle denotes the sample mean, while the black whiskers indicate the
 maximum and minimum values observed, respectively. **Figure 4a, 4b)** Time
 lapse of real experiments with the Double wall and Stepscape right
 scenarios. A(i-iii) and B(i-iii) show the robot’s speed, joint torques,
 and angles during the experiments (A) and (B), respectively. ### Notes *
 The dataset and scripts are designed to help reproduce key experimental
 results and figures from the paper. * The Python scripts were developed
 and tested using **Python 3.8**.