Multiple task optimization with a mixture of controllers for motion generation

Dehio, Niels; Reinhart, René Felix; Steil, Jochen J.

doi:10.1109/iros.2015.7354294

Cited by 26 publications

(29 citation statements)

References 15 publications

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“…Exceptions include frameworks based on reinforcement learning (RL) [16], [17] and LfD [11], where the importance of each space is learned. Approaches like [16], [17] employ stochastic optimization, given a set of reward functions related to high level goals, to find optimal weights. In [11], the authors treat the problem as a weighted least squares problem where the weights associated with each space, encoded as full precision matrices, reflect the variability and correlations in the demonstrations.…”

Section: B Simultaneous Learning Of Operational and Configuration Spmentioning

confidence: 99%

Learning Task Priorities from Demonstrations

et al. 2019

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Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.

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Section: B Simultaneous Learning Of Operational and Configuration Spmentioning

confidence: 99%

Learning Task Priorities from Demonstrations

et al. 2019

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“…where Γ p are weight matrices that regulate the contribution of each individual controller. Examples of Γ p found in the literature include scalar terms that maximize external rewards [20] and precision matrices, either computed from covariance [6], [19] or uncertainty [7]. Equation (12) has an analytical solution given byû =Σ u P p=1 Γ p u p , wherê…”

Section: Fusing Optimal Controllersmentioning

confidence: 99%

Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives

Silvério

Huang

Abu‐Dakka

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty using a single model. This rich set of information is used in combination with the fusion of optimal controllers to learn robot actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains otherwise.

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“…The problem of combining controllers can be broadly divided into two types of approaches. In [2], [3], [4], the authors use a weighted combination of individual torque controllers, with each controller responsible for a particular sub-task (e.g. balance, manipulation, joint limit avoidance).…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by works in which a combination of torque controllers results in a flexible importance assignment and smooth transitions between different tasks [2], [3], [4], we propose a strategy where the controller combination is learned from demonstrations. In this section we define the individual controllers that we exploit for configuration and operational space control.…”

Section: Operational Spacementioning

confidence: 99%

“…The problem of learning control commands and their respective importance is thus framed as the learning of reference trajectories as Gaussian distributions N (µ (p) , Σ (p) ), and generating Gaussian-distributed torque commands N (τ (p) , Σ (p) τ ), which encapsulate the control reference and its importance with respect to other controllers. In previous work, controller weights are either set empirically [2] or optimized through reinforcement learning [3], [4]. In contrast to these works, we employ probabilistic regression algorithms to learn N (µ (p) , Σ (p) ), and consequently N (τ (p) , Σ (p) τ ), ∀p = 1, ..., P , from human demonstrations.…”

Section: B From Probabilistic References To Probabilistic Torquesmentioning

confidence: 99%

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Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints

Silvério

Huang

Rozo

et al. 2018

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

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When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task space, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned probabilistically from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating new torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7-DoF torquecontrolled manipulators, with tasks that require the consideration of different controllers to be properly executed.

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Multiple task optimization with a mixture of controllers for motion generation

Cited by 26 publications

References 15 publications

Learning Task Priorities from Demonstrations

Learning Task Priorities from Demonstrations

Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives

Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints

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