Learning by Demonstration and Robust Control of Dexterous In-Hand Robotic Manipulation Skills

Solak, Gokhan; Jamone, Lorenzo

doi:10.1109/iros40897.2019.8967567

Cited by 20 publications

(16 citation statements)

References 21 publications

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“…Learning-based methods can complement the capabilities of analytic approaches, e.g. to obtain impedance gains for grasping and within-grasp manipulation [45], to hierarchically structure a cube manipulation problem [46], or to leverage dynamic movement primitives for within-grasp manipulation [47].…”

Section: E Data-driven Approachesmentioning

confidence: 99%

Surprisingly Robust In-Hand Manipulation: An Empirical Study

Bhatt

Sieler

Puhlmann

et al. 2021

Robotics: Science and Systems XVII

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We present in-hand manipulation skills on a dexterous, compliant, anthropomorphic hand. Even though these skills were derived in a simplistic manner, they exhibit surprising robustness to variations in shape, size, weight, and placement of the manipulated object. They are also very insensitive to variation of execution speeds, ranging from highly dynamic to quasi-static. The robustness of the skills leads to compositional properties that enable extended and robust manipulation programs. To explain the surprising robustness of the in-hand manipulation skills, we performed a detailed, empirical analysis of the skills' performance. From this analysis, we identify three principles for skill design: 1) Exploiting the hardware's innate ability to drive hard-to-model contact dynamics. 2) Taking actions to constrain these interactions, funneling the system into a narrow set of possibilities. 3) Composing such action sequences into complex manipulation programs. We believe that these principles constitute an important foundation for robust robotic in-hand manipulation, and possibly for manipulation in general.

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Section: E Data-driven Approachesmentioning

confidence: 99%

Surprisingly Robust In-Hand Manipulation: An Empirical Study

Bhatt

Sieler

Puhlmann

et al. 2021

Robotics: Science and Systems XVII

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show abstract

“…This force is calculated from coordinates of fingertip to virtual frame positioned in the middle of fingers and thumb. The concept of virtual springs is discussed in detail by Solak and Jamone (2019) . Equation of grasp force is:

where

is vector between coordinates of fingertip and virtual frame.…”

Section: Methodsmentioning

confidence: 99%

Grasp Stability Prediction for a Dexterous Robotic Hand Combining Depth Vision and Haptic Bayesian Exploration

et al. 2021

Self Cite

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Grasp stability prediction of unknown objects is crucial to enable autonomous robotic manipulation in an unstructured environment. Even if prior information about the object is available, real-time local exploration might be necessary to mitigate object modelling inaccuracies. This paper presents an approach to predict safe grasps of unknown objects using depth vision and a dexterous robot hand equipped with tactile feedback. Our approach does not assume any prior knowledge about the objects. First, an object pose estimation is obtained from RGB-D sensing; then, the object is explored haptically to maximise a given grasp metric. We compare two probabilistic methods (i.e. standard and unscented Bayesian Optimisation) against random exploration (i.e. uniform grid search). Our experimental results demonstrate that these probabilistic methods can provide confident predictions after a limited number of exploratory observations, and that unscented Bayesian Optimisation can find safer grasps, taking into account the uncertainty in robot sensing and grasp execution.

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

Section: Human-robot Co-manipulationmentioning

confidence: 99%

Dynamic Movement Primitives in Robotics: A Tutorial Survey

Saveriano¹,

Abu‐Dakka²,

Kramberger³

et al. 2021

Preprint

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Biological systems, including human beings, have the innate ability to perform complex tasks in versatile and agile manner. Researchers in sensorimotor control have tried to understand and formally define this innate property. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally lead 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, Dynamic Movement Primitives (DMPs) have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human-robot co-working, resulting a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMPs formulations in rigorous mathematical terms, and discuss 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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Learning by Demonstration and Robust Control of Dexterous In-Hand Robotic Manipulation Skills

Cited by 20 publications

References 21 publications

Surprisingly Robust In-Hand Manipulation: An Empirical Study

Surprisingly Robust In-Hand Manipulation: An Empirical Study

Grasp Stability Prediction for a Dexterous Robotic Hand Combining Depth Vision and Haptic Bayesian Exploration

Dynamic Movement Primitives in Robotics: A Tutorial Survey

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