Publications | Lorenzo Rapetti

Towards a Shared Embodied Intelligence of Humanoid Robots: Optimization, Development, and Testing of the Human-Aware ergoCub Robot

Wed, 16 Apr 2025 00:00:00 +0000

Collaboration is an inherent aspect of human behavior, enabling tasks that are otherwise impossible for individuals alone. This collaborative ability stems from the capacity of individuals to coordinate their actions by leveraging internal representations of their companions—a concept known as shared intelligence. Additionally, humans are characterized by physical bodies and cognitive abilities that are optimized in response to their environment, a phenomenon referred to as embodied cognition. In this paper, we propose an architecture that combines shared intelligence and embodied cognition to enable robots to physically collaborate with humans, where robot hardware and control are optimized for human metrics, using representations of the human body and motion intelligence. By doing so, our ultimate aim is to design humanoid robots that possess a degree of shared embodied intelligence. More specifically, the proposed architecture optimizes the robot’s hardware and physical intelligence parameters with respect to human ergonomic metrics. We achieve this by deriving models of human-robot interaction parameterized with respect to the robot’s hardware configurations and by incorporating human models into the robot’s physical intelligence. As a tangible outcome of the proposed architecture, we present the humanoid robot ergoCub, whose body and physical intelligence have been optimized for performing collaborative actions with humans. Our approach yields concrete results in designing humanoid robots that prioritize human ergonomics at both the hardware and physical intelligence levels. The potential applications of this approach span diverse fields, including industrial and assistive robotics.

iCub3 avatar system: Enabling remote fully immersive embodiment of humanoid robot

Wed, 24 Jan 2024 00:00:00 +0000

We present an avatar system designed to facilitate the embodiment of humanoid robots by human operators, validated through iCub3, a humanoid developed at the Istituto Italiano di Tecnologia. More precisely, the paper makes two contributions: First, we present the humanoid iCub3 as a robotic avatar that integrates the latest significant improvements after about 15 years of development of the iCub series. Second, we present a versatile avatar system enabling humans to embody humanoid robots encompassing aspects such as locomotion, manipulation, voice, and facial expressions with comprehensive sensory feedback including visual, auditory, haptic, weight, and touch modalities. We validated the system by implementing several avatar architecture instances, each tailored to specific requirements. First, we evaluated the optimized architecture for verbal, nonverbal, and physical interactions with a remote recipient. This testing involved the operator in Genoa and the avatar in the Biennale di Venezia, Venicethus allowing the operator to visit the Italian art exhibition remotely. Second, we evaluated the optimized architecture for recipient physical collaboration and public engagement on stage, live, at the We Make Future show, a prominent world digital innovation festival. In this instance, the operator was situated in Genoa while the avatar operated in Riminiinteracting with a recipient who entrusted the avatar with a payload to carry on stage before an audience of approximately 2000 spectators. Third, we present the architecture implemented by the iCub Team for the All Nippon Airways (ANA) Avatar XPrize competition. An avatar system to embody the humanoid robot iCub3 for remote verbal, nonverbal, and physical interaction is presented.

A Control Approach for Human-Robot Ergonomic Payload Lifting

Mon, 29 May 2023 00:00:00 +0000

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.

Shared Control of Robot-Robot Collaborative Lifting with Agent Postural and Force Ergonomic Optimization

Sun, 30 May 2021 00:00:00 +0000

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.

Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics

Tue, 20 Oct 2020 00:00:00 +0000

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for dynamically solving the inverse kinematics of highly articulate systems such as humans. The method presented is model-based, it makes use of velocity correction and differential kinematics integration in order to compute the system configuration. The convergence of the model towards the measurements is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution in real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.

Towards Partner-Aware Humanoid Robot Control Under Physical Interactions

Thu, 05 Sep 2019 00:00:00 +0000

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.