A learning-based shared control architecture for interactive task execution

Abi-Farraj, Firas; Osa, Takayuki; Peters, Nicolo Pedemonte Jan; Neumann, Gerhard; Giordano, Paolo Robuffo

doi:10.1109/icra.2017.7989042

Cited by 42 publications

(48 citation statements)

References 30 publications

(35 reference statements)

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“…According to the demonstrated observation sequence, GMR method is employed to generate a generalized task model after encoded by GMM. [40][41][42] By employing the joint probability distribution, the observation sequence, means matrix, and covariance matrix can be described as…”

Section: Task Generated By Gmrmentioning

confidence: 99%

A Task Learning Mechanism for the Telerobots

Luo

Yang

et al. 2019

Int. J. Human. Robot.

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Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challenging to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propose a task learning framework to represent the manipulation skill demonstrated by a remotely controlled robot. Gaussian mixture model is utilized to encode and parametrize the smooth task trajectory according to the observations from the demonstrations. After encoding the demonstrated trajectory, a new task trajectory is generated based on the variability information of the learned model. Experimental results have demonstrated the feasibility of the proposed method.

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Section: Task Generated By Gmrmentioning

confidence: 99%

A Task Learning Mechanism for the Telerobots

Luo

Yang

et al. 2019

Int. J. Human. Robot.

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“…In these approaches, the new test is compared to what the robot has experienced, and new demonstrations are requested when the robot deems it necessary. On the other hand, Abi-Farraj et al considered generalized trajectories for refining the learned distribution via an information gain threshold so that the robot does not need to request additional demonstrations [18].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement

Kuchenbecker

2019

Applied Bionics and Biomechanics

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Learning from demonstration (LfD) enables a robot to emulate natural human movement instead of merely executing preprogrammed behaviors. This article presents a hierarchical LfD structure of task-parameterized models for object movement tasks, which are ubiquitous in everyday life and could benefit from robotic support. Our approach uses the task-parameterized Gaussian mixture model (TP-GMM) algorithm to encode sets of demonstrations in separate models that each correspond to a different task situation. The robot then maximizes its expected performance in a new situation by either selecting a good existing model or requesting new demonstrations. Compared to a standard implementation that encodes all demonstrations together for all test situations, the proposed approach offers four advantages. First, a simply defined distance function can be used to estimate test performance by calculating the similarity between a test situation and the existing models. Second, the proposed approach can improve generalization, e.g., better satisfying the demonstrated task constraints and speeding up task execution. Third, because the hierarchical structure encodes each demonstrated situation individually, a wider range of task situations can be modeled in the same framework without deteriorating performance. Last, adding or removing demonstrations incurs low computational load, and thus, the robot's skill library can be built incrementally. We first instantiate the proposed approach in a simulated task to validate these advantages. We then show that the advantages transfer to real hardware for a task where naive participants collaborated with a Willow Garage PR2 robot to move a handheld object. For most tested scenarios, our hierarchical method achieved significantly better task performance and subjective ratings than both a passive model with only gravity compensation and a single TP-GMM encoding all demonstrations.

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“…Incremental learning approaches focus on the learning of a task as a whole through several interactions with the environment or the human user; see [6], [7], and [8]. Several techniques can be envisioned to accommodate the new experiences.…”

Section: Introductionmentioning

confidence: 99%

From Human Physical Interaction To Online Motion Adaptation Using Parameterized Dynamical Systems

Khoramshahi¹,

Laurens²,

Triquet³

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this work, we present an adaptive motion planning approach for impedance-controlled robots to modify their tasks based on human physical interactions. We use a class of parameterized time-independent dynamical systems for motion generation where the modulation of such parameters allows for motion flexibility. To adapt to human interactions, we update the parameters of our dynamical system in order to reduce the tracking error (i.e., between the desired trajectory generated by the dynamical system and the real trajectory influenced by the human interaction). We provide analytical analysis and several simulations of our method. Finally, we investigate our approach through real world experiments with a 7-DOF KUKA LWR 4+ robot performing tasks such as polishing and pick-and-place.

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A learning-based shared control architecture for interactive task execution

Cited by 42 publications

References 30 publications

A Task Learning Mechanism for the Telerobots

A Task Learning Mechanism for the Telerobots

Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement

From Human Physical Interaction To Online Motion Adaptation Using Parameterized Dynamical Systems

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