<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Paper-Conference | Lorenzo Rapetti</title><link>https://lrapetti.github.io/publication_types/paper-conference/</link><atom:link href="https://lrapetti.github.io/publication_types/paper-conference/index.xml" rel="self" type="application/rss+xml"/><description>Paper-Conference</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 29 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://lrapetti.github.io/media/icon_hu_982c5d63a71b2961.png</url><title>Paper-Conference</title><link>https://lrapetti.github.io/publication_types/paper-conference/</link></image><item><title>A Control Approach for Human-Robot Ergonomic Payload Lifting</title><link>https://lrapetti.github.io/publications/a-control-approach/</link><pubDate>Mon, 29 May 2023 00:00:00 +0000</pubDate><guid>https://lrapetti.github.io/publications/a-control-approach/</guid><description>&lt;p&gt;Collaborative robots can relief human operators from excessive efforts during payload lifting activities. Modelling the human partner allows the design of safe and efficient collaborative strategies. In this paper, we present a control approach for human-robot collaboration based on human monitoring through whole-body wearable sensors, and interaction modelling through coupled rigid-body dynamics. Moreover, a trajectory advancement strategy is proposed, allowing for online adaptation of the robot trajectory depending on the human motion. The resulting framework allows us to perform payload lifting tasks, taking into account the ergonomic requirements of the agents. Validation has been performed in an experimental scenario using the iCub3 humanoid robot and a human subject sensorized with the iFeel wearable system.&lt;/p&gt;</description></item><item><title>Shared Control of Robot-Robot Collaborative Lifting with Agent Postural and Force Ergonomic Optimization</title><link>https://lrapetti.github.io/publications/shared-control-robot/</link><pubDate>Sun, 30 May 2021 00:00:00 +0000</pubDate><guid>https://lrapetti.github.io/publications/shared-control-robot/</guid><description>&lt;p&gt;Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking into account the ergonomic requirements of the robots through the optimization of posture and contact forces. The framework is validated in an experimental scenario with two iCub humanoid robots performing different payload lifting sequences.&lt;/p&gt;</description></item><item><title>Towards Partner-Aware Humanoid Robot Control Under Physical Interactions</title><link>https://lrapetti.github.io/publications/towards-partner-aware/</link><pubDate>Thu, 05 Sep 2019 00:00:00 +0000</pubDate><guid>https://lrapetti.github.io/publications/towards-partner-aware/</guid><description>&lt;p&gt;The topic of physical human-robot interaction received a lot of attention from the robotics community because of many promising application domains. However, studying physical interaction between a robot and an external agent, like a human or another robot, without considering the dynamics of both the systems may lead to many shortcomings in fully exploiting the interaction. In this paper, we present a coupled-dynamics formalism followed by a sound approach in exploiting helpful interaction with a humanoid robot. In particular, we propose the first attempt to define and exploit the human help for the robot to accomplish a specific task. As a result, we present a task-based partner-aware robot control techniques. The theoretical results are validated by conducting experiments with two iCub humanoid robots involved in physical interaction.&lt;/p&gt;</description></item></channel></rss>