# Vision-Based Online Key Point Estimation of Deformable Robots

arXiv: https://arxiv.org/abs/2211.05222 · HTML: https://mlsanity.com/paper/2211.05222/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control](https://mlsanity.com/paper/2411.00138/) (2024)
- [Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots](https://mlsanity.com/paper/2504.13582/) (2025)
- [Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video](https://mlsanity.com/paper/2511.18322/) (2025)

## Abstract (arXiv)

The precise control of soft and continuum robots requires knowledge of their shape, which has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built-in sensors resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on expensive tracking systems with reflective markers placed on all components, which are infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, a regression approach is presented for 3D key point estimation using a convolutional neural network. The proposed approach takes advantage of data-driven supervised learning and is capable of online marker-less estimation during inference. Two images of a robotic system are taken simultaneously at 25 Hz from two different perspectives, and are fed to the network, which returns for each pair the parameterized key point or PCC shape representations. The proposed approach outperforms marker-less state-of-the-art methods by a maximum of 4.5% in estimation accuracy while at the same time being more robust and requiring no prior knowledge of the shape. Online evaluations on two types of soft robotic arms and a soft robotic fish demonstrate our method's accuracy and versatility on highly deformable systems.