The need for users' safety and technology accept-ability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions. Classic solutions for anthropomorphic movement generation usually rely on optimization procedures, which build upon hypotheses devised from neuroscientific literature, or capitalize on learning methods. However, these approaches come with limitations, e.g. limited motion variability or the need for high dimensional datasets. In this work, we present a technique to directly embed human upper limb principal motion modes computed through functional analysis in the robot trajectory optimization. We report on the Implementation with manipulators with redundant anthropomorphic kinematic architectures - although dissimilar with respect to the human model used for functional mode extraction - via Cartesian impedance control. In our experiments, we show how human trajectories mapped onto a robotic manipulator still exhibit the main characteristics of human-likeness, e.g. low jerk values. We discuss the results with respect to the state of the art, and their implications for advanced human-robot interaction in industrial co-botics and for human assistance.

A technical framework for human-like motion generation with autonomous anthropomorphic redundant manipulators

Averta G.;Bicchi A.;Bianchi M.
2020

Abstract

The need for users' safety and technology accept-ability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions. Classic solutions for anthropomorphic movement generation usually rely on optimization procedures, which build upon hypotheses devised from neuroscientific literature, or capitalize on learning methods. However, these approaches come with limitations, e.g. limited motion variability or the need for high dimensional datasets. In this work, we present a technique to directly embed human upper limb principal motion modes computed through functional analysis in the robot trajectory optimization. We report on the Implementation with manipulators with redundant anthropomorphic kinematic architectures - although dissimilar with respect to the human model used for functional mode extraction - via Cartesian impedance control. In our experiments, we show how human trajectories mapped onto a robotic manipulator still exhibit the main characteristics of human-likeness, e.g. low jerk values. We discuss the results with respect to the state of the art, and their implications for advanced human-robot interaction in industrial co-botics and for human assistance.
978-1-7281-7395-5
File in questo prodotto:
File Dimensione Formato  
paper_planning_HL_robots_IC_ICRA2020 (2).pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 3.88 MB
Formato Adobe PDF
3.88 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1059132
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 6
social impact