# Multifunctional physical reservoir computing in soft tensegrity robots

arXiv: https://arxiv.org/abs/2507.21496 · HTML: https://mlsanity.com/paper/2507.21496/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 124 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 28 of 28)

- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [Machine learning prediction of critical transition and system collapse](https://mlsanity.com/paper/2012.01545/) (2020)
- [Dynamic Movement Primitives in Robotics: A Tutorial Survey](https://mlsanity.com/paper/2102.03861/) (2021)
- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [Anticipating synchronization with machine learning](https://mlsanity.com/paper/2103.13358/) (2021)
- [Multifunctionality in a Reservoir Computer](https://mlsanity.com/paper/2008.06348/) (2020)
- [Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing](https://mlsanity.com/paper/2108.04074/) (2021)
- [A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systems](https://mlsanity.com/paper/1906.04608/) (2019)
- [Designing spontaneous behavioral switching via chaotic itinerancy](https://mlsanity.com/paper/2002.08332/) (2020)
- [Seeing double with a multifunctional reservoir computer](https://mlsanity.com/paper/2305.05799/) (2023)
- [Harnessing disordered quantum dynamics for machine learning](https://mlsanity.com/paper/1602.08159/) (2016)
- [Learning Temporal Quantum Tomography](https://mlsanity.com/paper/2103.13973/) (2021)
- [Exploiting short-term memory in soft body dynamics as a computational resource](https://mlsanity.com/paper/1406.4929/) (2014)
- [Physical Reservoir Computing with Origami and its Application to Robotic Crawling](https://mlsanity.com/paper/2101.08348/) (2021)
- [Embedding bifurcations into pneumatic artificial muscle](https://mlsanity.com/paper/2305.03994/) (2023)
- [Confabulation dynamics in a reservoir computer: Filling in the gaps with untrained attractors](https://mlsanity.com/paper/2505.04792/) (2025)
- [Bio-inspired Tensegrity Soft Modular Robots](https://mlsanity.com/paper/1703.10139/) (2017)
- [Creating New Chaotic Signals with Reservoir Computers](https://mlsanity.com/paper/2210.06250/) (2022)
- [Multifunctionality in a Connectome-Based Reservoir Computer](https://mlsanity.com/paper/2306.01885/) (2023)
- [Designing Chaotic Attractors: A Semi-supervised Approach](https://mlsanity.com/paper/2407.09545/) (2024)
- [Adaptive and Resilient Soft Tensegrity Robots](https://mlsanity.com/paper/1702.03258/) (2017)
- [Peristaltic locomotion without digital controllers: Exploiting the origami multi-stability to coordinate robotic motions](https://mlsanity.com/paper/1906.04091/) (2019)
- [Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks](https://mlsanity.com/paper/2302.05517/) (2023)
- [Behavioral Repertoires for Soft Tensegrity Robots](https://mlsanity.com/paper/2009.10864/) (2020)
- [Thermodynamic limit in learning period three](https://mlsanity.com/paper/2405.08825/) (2024)
- [Trans-Bifurcation Prediction of Dynamics in terms of Extreme Learning Machines with Control Inputs](https://mlsanity.com/paper/2410.13289/) (2024)

## Built on by (most cited first, 2 of 2)

- [Storage and selection of multiple chaotic attractors in minimal reservoir computers](https://mlsanity.com/paper/2603.15155/) (2026)
- [On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification](https://mlsanity.com/paper/2604.21602/) (2026)

## Abstract (arXiv)

Recent studies have demonstrated that the dynamics of physical systems can be utilized for the desired information processing under the framework of physical reservoir computing (PRC). Robots with soft bodies are examples of such physical systems, and their nonlinear body-environment dynamics can be used to compute and generate the motor signals necessary for the control of their own behavior. In this simulation study, we extend this approach to control and embed not only one but also multiple behaviors into a type of soft robot called a tensegrity robot. The resulting system, consisting of the robot and the environment, is a multistable dynamical system that converges to different attractors from varying initial conditions. Furthermore, attractor analysis reveals that there exist "untrained attractors" in the state space of the system outside the training data. These untrained attractors reflect the intrinsic properties and structures of the tensegrity robot and its interactions with the environment. The impacts of these recent findings in PRC remain unexplored in embodied AI research. We here illustrate their potential to understand various features of embodied cognition that have not been fully addressed to date.