Abstract
Motivated by the deployment of multi-legged walking robots in traversing various terrain types, we benchmark existing online and unsupervised incremental learning approaches in traversal cost prediction. The traversal cost is defined by the proprioceptive signal of the robot traversal stability that is combined with appearance and geometric properties of the traversed terrains to construct the traversal cost model incrementally. In the motivational deployment, such a model is instantaneously utilized to extrapolate the traversal cost for observed areas that have not yet been visited by the robot to avoid difficult terrains in motion planning. The examined approaches are Incremental Gaussian Mixture Network, Growing Neural Gas, Improved Self-Organizing Incremental Neural Network, Locally Weighted Projection Regression, and Bayesian Committee Machine with Gaussian Process Regressors. The performance is examined using a dataset of the various terrains traversed by a real hexapod walking robot. A part of the presented benchmarking is thus a description of the dataset and also a construction of the reference traversal cost model that is used for comparison of the evaluated regressors. The reference is designed as a compound Gaussian process-based model that is learned separately over the individual terrain types. Based on the evaluation results, the best performance among the examined regressors is provided by Incremental Gaussian Mixture Network, Improved Self-Organizing Incremental Neural Network, and Locally Weighted Projection Regression, while the latter two have the lower computational requirements.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bartoszyk, S., Kasprzak, P., Belter, D.: Terrain-aware motion planning for a walking robot. In: International Workshop on Robot Motion and Control (RoMoCo), IEEE, pp. 29–34 (2017). https://doi.org/10.1109/RoMoCo.2017.8003889
Brunner, M., Brüggemann, B., Schulz, D.: Rough Terrain Motion Planning for Actuated, Tracked Robots. In: International Conference on Agents and Artificial Intelligence (ICAART), pp. 40–61 (2013). https://doi.org/10.1007/978-3-662-44440-5_3
Deisenroth, M.P., Ng, J.W.: Distributed Gaussian processes. In: International Conference on International Conference on Machine Learning (ICML), pp. 1481–1490 (2015)
Faigl, J., Prágr, M.: Incremental traversability assessment learning using growing neural gas algorithm. In: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, pp. 166–176 (2020). https://doi.org/10.1007/978-3-030-19642-4_17
Fritzke, B.: A growing neural gas network learns topologies. In: Neural Information Processing Systems (NIPS), pp. 625–632 (1994)
GPy: A Gaussian process framework in Python (2012). http://github.com/SheffieldML/GPy. Accessed 28 Mar 2019
Kragh, M., Jørgensen, R.N., Pedersen, H.: Object detection and terrain classification in agricultural fields using 3D lidar data. In: International Conference on Computer Vision Systems (ICVS), vol. 9163, pp. 188–197 (2015). https://doi.org/10.1007/978-3-319-20904-3_18
LWPR library (2007). https://github.com/jdlangs/lwpr. Accessed 28 May 2019
Nowicki, M.R., Belter, D., Kostusiak, A., Čížek, P., Faigl, J., Skrzypczynski, P.: An experimental study on feature-based SLAM for multi-legged robots with RGB-D sensors. Ind. Robot 44(4), 428–441 (2017). https://doi.org/10.1108/IR-11-2016-0340
O’Callaghan, S., Ramos, F.T., Durrant-Whyte, H.: Contextual occupancy maps using Gaussian processes. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1054–1060 (2009). https://doi.org/10.1109/ROBOT.2009.5152754
Pinto, R., Engel, P., Alegre, P.: A fast incremental Gaussian mixture model. PLoS ONE e0141942 (2015). https://doi.org/10.1371/journal.pone.0139931
Prágr, M., Čížek, P., Bayer, J., Faigl, J.: Online incremental learning of the terrain traversal cost in autonomous exploration. In: Robotics: Science and Systems (RSS) (2019). https://doi.org/10.15607/RSS.2019.XV.040
Prágr, M., Čížek, P., Faigl, J.: Cost of transport estimation for legged robot based on terrain features inference from aerial scan. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1745–1750 (2018). https://doi.org/10.1109/IROS.2018.8593374
Prágr, M., Čížek, P., Faigl, J.: Incremental learning of traversability cost for aerial reconnaissance support to ground units. In: Modelling and Simulation for Autonomous Systems (MESAS), pp. 412–421 (2019). https://doi.org/10.1007/978-3-030-14984-0_30
Shen, F., Yu, H., Sakurai, K., Hasegawa, O.: An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network. Neural Comput. Appl. 20(7), 1061–1074 (2011). https://doi.org/10.1007/s00521-010-0428-y
Tresp, V.: A Bayesian committee machine. Neural Comput. 12(11), 2719–2741 (2000). https://doi.org/10.1162/089976600300014908
Tucker, V.A.: The energetic cost of moving about: walking and running are extremely inefficient forms of locomotion. Much greater efficiency is achieved by birds, fish-and bicyclists. Am. Sci. 63(4), 413–419 (1975)
Vijayakumar, S., Schaal, S.: Locally weighted projection regression: an O(n) algorithm for incremental real time learning in high dimensional space. In: International Conference on International Conference on Machine Learning (ICML), pp. 1079–1086 (2000)
Xiang, Z., Xiao, Z., Wang, D., Xiao, J.: Gaussian kernel smooth regression with topology learning neural networks and Python implementation. Neurocomputing 260, 1–4 (2017). https://doi.org/10.1016/j.neucom.2017.01.051
Čížek, P., Masri, D., Faigl, J.: Foothold placement planning with a hexapod crawling robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4096–4101 (2017). https://doi.org/10.1109/IROS.2017.8206267
Acknowledgments
This work was supported by the Czech Science Foundation under research project No. 18-18858S.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Prágr, M., Faigl, J. (2019). Benchmarking Incremental Regressors in Traversal Cost Assessment. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_52
Download citation
DOI: https://doi.org/10.1007/978-3-030-30487-4_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30486-7
Online ISBN: 978-3-030-30487-4
eBook Packages: Computer ScienceComputer Science (R0)