Continuum Robot Modeling with Action Conditioned Flow Matching

TL;DR

This paper introduces a data-driven, action-conditioned flow matching model for predicting the steady-state shape of tendon-driven continuum robots, validated through simulations and real hardware experiments.

Contribution

It presents a novel self-modeling approach that accurately predicts TDCR shapes from actuation, including payload effects, using a lightweight 3D printed platform and RGB-D data.

Findings

Improved shape prediction accuracy over prior methods.

Model generalizes to payload conditions in simulation.

Effective in both simulated and real hardware experiments.

Abstract

Predicting the shape of tendon driven continuum robots (TDCRs) at steady state from actuation remains challenging due to continuous deformation, complex tendon routing, compliance, friction, and fabrication variability. In this paper, we address this problem as kinematic self modeling conditioned on action. We present a lightweight 3D printed TDCR hardware platform and an RGB-D data collection pipeline with multiple cameras, and we learn a point cloud flow matching model that maps motor actuation states to the robot's settled 3D geometry. The model is trained from randomly sampled quasi static configurations and evaluated on test motor commands within the same TDCR design family and actuation range. We compare against prior 3D deformable object and robot self modeling approaches in both MuJoCo simulation and real hardware experiments. Experiments on simulated 2-, 3-, and 5-module TDCRs and real 2- and 3-module robots show improved shape prediction accuracy under CD and EMD metrics. We further show in simulation that the same conditional formulation generalizes to tip payload as a conditioning input, enabling payload conditioned steady-state shape prediction. These results demonstrate a data driven self modeling framework for quasi static TDCR geometry prediction.