English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many robots utilize commercial force/torque sensors to identify inertialproperties of unknown objects. However, such sensors can be difficult to applyto small-sized robots due to their weight, size, and cost. In this paper, wepropose a learning-based approach for estimating the mass and center of mass(COM) of unknown objects without using force/torque sensors at the end-effectoror on the joints. In our method, a robot arm carries an unknown object as itmoves through multiple discrete configurations. Measurements are collected whenthe robot reaches each discrete configuration and stops. A neural network isdesigned to estimate joint torques from encoder discrepancies. Given multiplesamples, we derive the closed-form relation between joint torques and theobject's inertial properties. Based on the derivation, the mass and COM ofobject are identified by weighted least squares. In order to improve theaccuracy of inferred inertial properties, an attention model is designed togenerate weights of joints, which indicate the relative importance for eachjoint. Our framework requires only encoder measurements without using anyforce/torque sensors, but still maintains accurate estimation capability. Theproposed approach has been demonstrated on a 4 degree of freedom (DOF) robotarm.

A Learning-Based Approach for Estimating Inertial Properties of Unknown Objects from Encoder Discrepancies