# Robot kinematic structure classification from time series of visual data

## TL;DR

This paper introduces a new algorithm that identifies robot kinematic structures from visual data time series, determining link sequences, joint types, and input signals with lower computational costs.

## Contribution

It presents a novel, efficient algorithm capable of identifying both the kinematic chain and joint types from visual observations, advancing prior methods.

## Key findings

- Reduces computational costs compared to existing methods.
- Successfully identifies joint types along with the kinematic chain.
- Applicable to visual data time series for robot structure identification.

## Abstract

In this paper we present a novel algorithm to solve the robot kinematic structure identification problem. Given a time series of data, typically obtained processing a set of visual observations, the proposed approach identifies the ordered sequence of links associated to the kinematic chain, the joint type interconnecting each couple of consecutive links, and the input signal influencing the relative motion. Compared to the state of the art, the proposed algorithm has reduced computational costs, and is able to identify also the joints' type sequence.