AIConfPaper
← Search

Fotios Dimeas

6 accepted papers

2023

Enforcing Constraints for Dynamic Obstacle Avoidance by Compliant Robots

Leonidas Koutras, Konstantinos Vlachos, George S. Kanakis, Fotios Dimeas, Zoe Doulgeri, George A. Rovithakis
ICRA 2023poster

In this work a control scheme is proposed to enforce dynamic obstacle avoidance constraints to the full body of actively compliant robots. We argue that both compliance and accuracy are necessary to build safe collaborative robotic systems; obstacle avoidance is usually not enough, due to the relian…

Cited by 3Source ↗Scholar ↗
2020

Progressive automation of periodic tasks on planar surfaces of unknown pose with hybrid force/position control

Fotios Dimeas, Zoe Doulgeri
IROS 2020poster

This paper presents a teaching by demonstration method for contact tasks with periodic movement on planar surfaces of unknown pose. To learn the motion on the plane, we utilize frequency oscillators with periodic movement primitives and we propose modified adaptation rules along with an extraction m…

Cited by 13Source ↗Scholar ↗
2018

A Frequency Domain Approach for Contact Type Distinction in Human-Robot Collaboration

Alexandros Kouris, Fotios Dimeas, Nikos A. Aspragathos
RA-L 2018

This letter addresses the problem of distinguishing unexpected collisions from voluntary contacts during human-robot collaboration. The objective of this letter is to improve the safety of the operator during the interaction with the robot. Based on the observation that cooperation forces present di

Cited by 39Source ↗Scholar ↗
2016

Manipulator performance constraints in Cartesian admittance control for human-robot cooperation

Fotios Dimeas, Vassilis C. Moulianitis, Charalampos Papakonstantinou, Nikos A. Aspragathos
ICRA 2016

This paper addresses the problem of providing feedback to the operator about the manipulator's performance during human-robot physical interaction. A method is proposed that implements virtual constraints in Cartesian admittance control in order to prevent the operator from guiding the manipulator t

Cited by 25Source ↗Scholar ↗
2015

Reinforcement learning of variable admittance control for human-robot co-manipulation

Fotios Dimeas, Nikos Aspragathos
IROS 2015poster

In this paper, a variable admittance controller based on reinforcement learning is proposed for human-robot co-manipulation tasks. Setting as the goal of the reinforcement learning algorithm the minimisation of the jerk throughout a point-to-point movement, the proposed controller can learn the appr…

Cited by 103Source ↗Scholar ↗