Interactive imitation learning of object movement skills

Mühlig, Manuel; Gienger, Michael; Steil, Jochen J.

doi:10.1007/s10514-011-9261-0

Cited by 65 publications

(45 citation statements)

References 49 publications

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“…In other cases, collision avoidance could be included in the generation of humanoid motion using the techniques developed in [31][32][33]. The vision-based path planning method for dual-arm end-effector features proposed by Qu et al [34] is also a useful tool for precise pose alignment of two objects, which is the position and orientation of the hands and equipment in our case.…”

Section: Handling Collisionmentioning

confidence: 99%

Human to humanoid motion conversion for dual-arm manipulation tasks

Tomić¹,

Chevallereau²,

Jovanović³

et al. 2018

Robotica

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SUMMARYA conversion process for the imitation of human dual-arm motion by a humanoid robot is presented. The conversion process consists of an imitation algorithm and an algorithm for generating human-like motion of the humanoid. The desired motions in Cartesian and joint spaces, obtained from the imitation algorithm, are used to generate the human-like motion of the humanoid. The proposed conversion process improves existing techniques and is developed with the aim to enable imitating of human motion with a humanoid robot, to perform a task with and/or without contact between hands and equipment. A comparative analysis shows that our algorithm, which takes into account the situation of marker frames and the position of joint frames, ensures more precise imitation than previously proposed methods. The results of our conversion algorithm are tested on the robot ROMEO through a complex "open/close drawer" task.

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Section: Handling Collisionmentioning

confidence: 99%

Human to humanoid motion conversion for dual-arm manipulation tasks

Tomić¹,

Chevallereau²,

Jovanović³

et al. 2018

Robotica

View full text Add to dashboard Cite

show abstract

“…[16,23,29,25,45,12,21]; 2) Approaches employing P models for the P frames of reference that are possibly relevant for the task, see e.g. [32,13]; 3) Approaches employing a single model whose parameters are modulated by task parameters, see e.g. [49,26,20].…”

Section: Adaptive Models Of Movementsmentioning

confidence: 99%

Robot Learning with Task-Parameterized Generative Models

Calinon

2017

Springer Proceedings in Advanced Robotics

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Task-parameterized models provide a representation of movement/behavior that can adapt to a set of task parameters describing the current situation encountered by the robot, such as location of objects or landmarks in its workspace. This paper gives an overview of the taskparameterized Gaussian mixture model (TP-GMM) presented in previous publications, and introduces a number of extensions and ongoing challenges required to move the approach toward unconstrained environments. In particular, it discusses its generalization capability and the handling of movements with a high number of degrees of freedom. It then shows that the method is not restricted to movements in task space, but that it can also be exploited to handle constraints in joint space, including priority constraints.

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“…Other approaches monitor with external sensing the actions of a human tutor and apply imitation learning methods to teach more complex tasks interactively (Billard et al, 2007;Kulic, Ott, Lee, Ishikawa, & Nakamura, 2011;Muehlig, Steil, & Gienger, 2012;Schaal et al, 2003) and, partially, also incrementally (Calinon & Billard, 2007b) in order to derive criteria to select a particular redundancy resolution.…”

Section: Related Workmentioning

confidence: 99%

A User Study on Kinesthetic Teaching of Redundant Robots in Task and Configuration Space

Wrede

Emmerich

Grünberg

et al. 2013

JHRI

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The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide a great degree of flexibility for the realization of complex applications, the flexibility gained generates the need for additional modeling steps and definition of criteria for redundancy resolution constraining the robot's movement generation. The explicit modeling of such criteria usually require experts to adapt the robot's movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching by guiding the robot to implicitly model the specific constraints in task and configuration space. We argue that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and show that typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, we propose a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. We evaluated this approach in a user study with 49 industrial workers at HARTING, a medium-sized manufacturing company. The results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space, and yield significantly improved performance for the teach-in of trajectories in task space.

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Interactive imitation learning of object movement skills

Cited by 65 publications

References 49 publications

Human to humanoid motion conversion for dual-arm manipulation tasks

Human to humanoid motion conversion for dual-arm manipulation tasks

Robot Learning with Task-Parameterized Generative Models

A User Study on Kinesthetic Teaching of Redundant Robots in Task and Configuration Space

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