# Deep Reinforcement Learning for Tensegrity Robot Locomotion

arXiv: https://arxiv.org/abs/1609.09049 · HTML: https://mlsanity.com/paper/1609.09049/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2016.

## Built on by (most cited first, 7 of 7)

- [Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine](https://mlsanity.com/paper/2209.06261/) (2022)
- [A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data](https://mlsanity.com/paper/2203.00041/) (2022)
- [Review of Cloud Service Composition for Intelligent Manufacturing](https://mlsanity.com/paper/2408.01795/) (2024)
- [Morphology-Aware Graph Reinforcement Learning for Tensegrity Robot Locomotion](https://mlsanity.com/paper/2510.26067/) (2025)
- [Don't Just Search, Understand: Semantic Path Planning Agent for Spherical Tensegrity Robots in Unknown Environments](https://mlsanity.com/paper/2511.01236/) (2025)
- [Hardware-Software Collaborative Computing of Photonic Spiking Reinforcement Learning for Robotic Continuous Control](https://mlsanity.com/paper/2512.00427/) (2025)
- [HierKick: Hierarchical Reinforcement Learning for Vision-Guided Soccer Robot Control](https://mlsanity.com/paper/2603.00948/) (2026)

## Abstract (arXiv)

Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However, control of tensegrity robots remains a difficult problem due to their unusual structures and complex dynamics. In this work, we show how locomotion gaits can be learned automatically using a novel extension of mirror descent guided policy search (MDGPS) applied to periodic locomotion movements, and we demonstrate the effectiveness of our approach on tensegrity robot locomotion. We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities. Our experiments demonstrate that our method not only learns fast, power-efficient feedback policies for rolling gaits, but that these policies can succeed with only the limited onboard sensing provided by SUPERball's accelerometers. We compare the learned feedback policies to learned open-loop policies and hand-engineered controllers, and demonstrate that the learned policy enables the first continuous, reliable locomotion gait for the real SUPERball robot. Our code and other supplementary materials are available from http://rll.berkeley.edu/drl_tensegrity