Target-Referred DMPs for Learning Bimanual Tasks from Shared-Autonomy Telemanipulation

Amadio, Fabio; Laghi, Marco; Raiano, Luigi; Rollo, Federico; Zunino, Andrea; Raiola, Gennaro; Ajoudani, Arash

doi:10.1109/humanoids53995.2022.10000233

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The initial pose, referred to as the home position, is selected taking into account factors such as the robot's dimensions and workspace. Subsequently, the picking pose is determined utilizing an artificial vision system [9] [1]. The selection of the placing pose considers the location of a designated storage cell.…”

Section: Resultsmentioning

confidence: 99%

Joint Position Bounds in Resolved-Acceleration Control: A Comparison

Testa,

Raiano,

Laghi

et al. 2024

Human-Friendly Robotics 2023

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The implementation of human-friendly robots is based on the deployment of robots that can safely and effectively work with humans in various environments. To this end, enforcing joint limits in planning and control play a fundamental role in avoiding the robot to exceed its physical constraint and preventing joint damages or failures that could lead to unpredictable behavior or compromised safety. However, the implementation of such limitations in instantaneous controllers is not trivial when position, velocity, and acceleration limits are all considered together. In this work, we compare three State-of-the-Art methods, namely the P-Step Ahead Predictor, the Control Barrier Function, and Invariance. Finally, we select the most performing one applied in a real use case based on a UR5e manipulator for a picking task where hitting joint limits may represent an issue.

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Section: Resultsmentioning

confidence: 99%

Joint Position Bounds in Resolved-Acceleration Control: A Comparison

Testa,

Raiano,

Laghi

et al. 2024

Human-Friendly Robotics 2023

Self Cite

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“…There are also other types of co-manipulation scenarios, such as within-hand bi-manipulation or human–robot object handover. For example, in Amadio et al (2022), Gao et al (2019), and Koene et al (2014) DMPs were used to perform bi-manipulation, while in Abdelrahman et al (2020), Iori et al (2023), Lafleche et al (2019), Prada et al (2014), and Solak and Jamone (2019) DMPs were used for human–robot object handover.…”

Section: Dmps In Application Scenariosmentioning

confidence: 99%

Dynamic movement primitives in robotics: A tutorial survey

Saveriano,

Abu-Dakka,

Kramberger

et al. 2023

The International Journal of Robotics Research

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Biological systems, including human beings, have the innate ability to perform complex tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to comprehend and formally define this innate characteristic. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally leads to the formulation of the motor primitives’ theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems and are well suited to generate motor commands for artificial systems like robots. In the last decades, DMPs have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human–robot co-working, resulting in a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMP formulations in rigorous mathematical terms and discuss the advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state-of-the-art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.

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