High-speed control and navigation for quadrupedal robots on complex and discrete terrain
Data files
Jun 10, 2025 version files 55.08 KB
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Doublewall.csv
17.45 KB
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fig_3c.py
744 B
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fig_3d.py
2.69 KB
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fig_4a.py
3.34 KB
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fig_4b.py
3.18 KB
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README.md
2.89 KB
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Stepscape_right.csv
22.72 KB
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Tracking_error.csv
2.07 KB
Abstract
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.
Dataset DOI: 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
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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.
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Stepscape_right.csv
Contains experimental logs collected from the "Stepscape" scenario. Similar toDoublewall.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.
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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.
