Papers

@inproceedings{granados_factor_graph_tensegrity,
title = {State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials},
author = {E Granados and P Meng and C Tang and S Sangani and W Johnson and R Kramer-Bottiglio and K Bekris},
year = {2026},
date = {2026-04-08},
booktitle = {9th IEEE-RAS International Conference on Soft Robotics (RoboSoft)},
address = {Kanazawa, Japan},
abstract = {Tensegrity robots offer compliance and adaptability, but their nonlinear, and underconstrained dynamics make state estimation challenging. Reliable continuous-time estimation of all rigid links is crucial for closed-loop control, system identification, and machine learning; however, conventional methods often fall short. This paper proposes a two-stage approach for robust state or trajectory estimation (i.e., filtering or smoothing) of a cable-driven tensegrity robot. For online state estimation, this work introduces a factor-graph-based method, which fuses measurements from an RGB-D camera with on-board cable length sensors. To the best of the authors’ knowledge, this is the first application of factor graphs in this domain. Factor graphs are a natural choice, as they exploit the robot’s structural properties and provide effective sensor fusion solutions capable of handling nonlinearities in practice. Both the Mahalanobis distance-based clustering algorithm, used to handle noise, and the Chebyshev polynomial method, used to estimate the most probable velocities and intermediate states, are shown to perform well on simulated and real-world data, compared to an ICP-based algorithm. Results show that the approach provides high fidelity, continuous-time state and trajectory estimates for complex tensegrity robot motions.},
keywords = {Dynamics, Estimation, Soft-Robots},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Bekris:2024aa,
title = {The State of Robot Motion Generation},
author = {K Bekris and J Doerr and P Meng and S Tangirala},
url = {https://arxiv.org/abs/2410.12172},
year = {2024},
date = {2024-12-01},
booktitle = {International Symposium of Robotics Research (ISRR)},
address = {Long Beach, California},
abstract = {This paper first reviews the large spectrum of methods for generating robot motion proposed over the 50 years of robotics research culminating to recent developments. It crosses the boundaries of methodologies, which are typically not surveyed together, from those that operate over explicit models to those that learn implicit ones. The paper concludes with a discussion of the current state-of-the-art and the properties of the varying methodologies highlighting opportunities for integration.},
keywords = {Dynamics, Learning, Planning},
pubstate = {published},
tppubtype = {inproceedings}
}