Scientific papers

Whole-Body Human Inverse Dynamics
IIT

Whole-Body Human Inverse Dynamics

We present experimental analysis on subjects performing a bowing task, and we estimate the motion and kinetics quantities.

iCub whole-body control
IIT

iCub whole-body control

This paper details the implementation of state-of-the-art whole-body control algorithms on the humanoid robot iCub. 

Computationally optimized dynamic computation
IIT

Computationally optimized dynamic computation

This paper describes an algorithm to perform dynamic computations . The method is computationally optimised with a sparse matrix factorisation.

Roadmap of the AnDy project
ALL

Roadmap of the AnDy project

This paper presents the goals and steps of the AnDy project, which leverages existing technologies to endow robots with the ability to control physical collaboration through intentional interaction.

The CoDyCo Project achievements and beyond

The CoDyCo Project achievements and beyond

This paper introduces the basis for human-in-the-loop robot controllers. We present a momentum-based torque controller exploiting human help.

Adaptive Whole-Body Manipulation in Retargeting

Adaptive Whole-Body Manipulation in Retargeting

This paper details a method for retargeting loco-manipulation motions from human demonstrations to humanoid robots. Our method allows the robot to adapt its motion to compensate for manipulated objects with different dynamics parameters.

Velocity-curvature patterns limit in PHRI

Velocity-curvature patterns limit in PHRI

The present study investigated how humans adapt to biological and non-biological velocity patterns in robot movements. Participants held the end-effector of a robot that traced an elliptic path with either biological (two-thirds power law) or non-biological velocity profiles.

Progress and prospects of the human–robot collaboration

Progress and prospects of the human–robot collaboration

The main purpose of this paper is to review the state-of-the-art on intermediate human–robot interfaces (bi-directional), robot control modalities, system stability, benchmarking and relevant use cases, and to extend views on the required future developments in the realm of human–robot collaboration.

Prediction of intention using Probabilistic Movement Primitives

Prediction of intention using Probabilistic Movement Primitives

This article describes our open-source software for predicting the intention of a user physically interacting with the humanoid robot iCub based on Probabilistic Movement Primitives (ProMPs), a versatile method for representing, generalizing, and reproducing complex motor skills.

Trial-and-Error Learning of Repulsors

Trial-and-Error Learning of Repulsors

In this paper, we introduce a trial-and-error learning algorithm that allows whole-body controllers to operate in spite of inaccurate models, without needing to update these models.

Safe trajectory optimization for whole-body motion of humanoids

Safe trajectory optimization for whole-body motion of humanoids

In this work, we optimize the task trajectories for whole-body balancing tasks with switching contacts, ensuring that the optimized movements are safe and never violate any of the robot and problem constraints. We use (1+1)-CMA-ES with Constrained Covariance Adaptation as a constrained black box stochastic optimization algorithm, with an instance of (1+1)- CMA-ES for bootstrapping the search.

Multi-modal intention prediction with Probabilistic Movement Primitives

Multi-modal intention prediction with Probabilistic Movement Primitives

This paper proposes a method for multi-modal prediction of intention based on a probabilistic description of movement primitives and goals. We target dyadic interaction between a human and a robot in a collaborative scenario.

Simulating Physiological Discomfort of Exoskeletons
ABT

Simulating Physiological Discomfort of Exoskeletons

This paper describes how to use musculoskeletal modelling to simulate physiological discomfort of exoskeletons. We introduce three biomechanical discomfort measures, muscle activation effort, representative joint reaction force and total metabolic cost,
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EUAn.Dy has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 731540