Co-manipulation with multiple probabilistic virtual guides

Raiola, Gennaro; Lamy, Xavier; Stulp, Freek

doi:10.1109/iros.2015.7353107

Cited by 36 publications

(38 citation statements)

References 18 publications

(41 reference statements)

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“…For example, the work in [6] proposes an approach for modeling of virtual guides in co-manipulation examples. They show how multiple guides can be used in pick-and-place tasks of heavy objects and how the combination and switching between virtual guides can reduce the need of manual tuning and a priori expert design.…”

Section: Related Workmentioning

confidence: 99%

Learning assistive teleoperation behaviors from demonstration

Havoutis

Calinon

2016

2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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Abstract-Emergency response in hostile environments often involves remotely operated vehicles (ROVs) that are teleoperated as interaction with the environment is typically required. Many ROV tasks are common to such scenarios and are often recurrent. We show how a probabilistic approach can be used to learn a task behavior model from data. Such a model can then be used to assist an operator performing the same task in future missions. We show how this approach can capture behaviors (constraints) that are present in the training data, and how this model can be combined with the operator's input online. We present an illustrative planar example and elaborate with a non-Destructive testing (NDT) scanning task on a teleoperation mock-up using a two-armed Baxter robot. We demonstrate how our approach can learn from examples task specific behaviors and automatically control the overall system, combining the operator's input and the learned model online, in an assistive teleoperation manner. This can potentially reduce the time and effort required to perform teleoperation tasks that are commonplace to ROV missions in the context of security, maintenance and rescue robotics.

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Section: Related Workmentioning

confidence: 99%

Learning assistive teleoperation behaviors from demonstration

Havoutis

Calinon

2016

2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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“…In a comanipulation context, the user directly interacts with the robot through physical contact (Raiola et al, 2015a;Becker et al, 2013;Dumora, 2014;Pezzementi et al, 2007). This allows a direct interaction between the robot and the user, and a more intuitive ability to perform the task since the user is better integrated in the procedure compared to the case where the user interacts with the environment through a teleoperated robot.…”

Section: Related Workmentioning

confidence: 99%

“…The GMM is initially trained from user demonstrations, using the approach described in Raiola et al, 2015a), and briefly repeated here. The demonstrated trajectories consist of samples…”

Section: Training the Gaussian Mixture Modelmentioning

confidence: 99%

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Co-manipulation with a library of virtual guiding fixtures

et al. 2017

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Virtual guiding fixtures constrain the movements of a robot to task-relevant trajectories, and have been successfully applied to, for instance, surgical and manufacturing tasks. Whereas previous work has considered guiding fixtures for single tasks, in this paper we propose a library of guiding fixtures for multiple tasks, and propose methods for 1) Creating and adding guides based on machine learning; 2) Selecting guides on-line based on probabilistic implementation of guiding fixtures; 3) Refining existing guides based on an incremental learning method. We demonstrate in an industrial task that a library of guiding fixtures provides an intuitive haptic interface for joint human-robot completion of tasks, and improves performance in terms of task execution time, mental workload and errors.

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“…These are virtual elements that model knowledge about some aspect of the task and are used to guide, limit, or assist the operator when performing the task [6], [7]. Recently [8] demonstrated how virtual fixtures can be learned from data and how new fixtures can be added to the system so that it can adapt to new manipulation examples. In our recent work in shared control [9], we showed how a teleoperation system can be designed to rely on learned probabilistic models of manipulation tasks in combination with online operator's input.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Supervisory teleoperation with online learning and optimal control

Havoutis

Calinon

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We present a general approach for online learning and optimal control of manipulation tasks in a supervisory teleoperation context, targeted to underwater remotely operated vehicles (ROVs). We use an online Bayesian nonparametric learning algorithm to build models of manipulation motions as task-parametrized hidden semi-Markov models (TP-HSMM) that capture the spatiotemporal characteristics of demonstrated motions in a probabilistic representation. Motions are then executed autonomously using an optimal controller, namely a model predictive control (MPC) approach in a receding horizon fashion. This way the remote system locally closes a high-frequency control loop that robustly handles noise and dynamically changing environments. Our system automates common and recurring tasks, allowing the operator to focus only on the tasks that genuinely require human intervention. We demonstrate how our solution can be used for a hot-stabbing motion in an underwater teleoperation scenario. We evaluate the performance of the system over multiple trials and compare with a state-of-the-art approach. We report that our approach generalizes well with only a few demonstrations, accurately performs the learned task and adapts online to dynamically changing task conditions.

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Co-manipulation with multiple probabilistic virtual guides

Cited by 36 publications

References 18 publications

Learning assistive teleoperation behaviors from demonstration

Learning assistive teleoperation behaviors from demonstration

Co-manipulation with a library of virtual guiding fixtures

Supervisory teleoperation with online learning and optimal control

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