Author name cluster

Javier R. Movellan

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7 papers

1 author row

IROS Conference 2012 Conference Paper

Control by Gradient Collocation: Applications to optimal obstacle avoidance and minimum torque control

Paul Ruvolo
Tingfan Wu
Javier R. Movellan

We present a new machine learning algorithm for learning optimal feedback control policies to guide a robot to a goal in the presence of obstacles. Our method works by first reducing the problem of obstacle avoidance to a continuous state, action, and time control problem, and then uses efficient collocation methods to solve for an optimal feedback control policy. This formulation of the obstacle avoidance problem improves over standard approaches, such as potential field methods, by being resistant to local minima, allowing for moving obstacles, handling stochastic systems, and computing feedback control strategies that take into account the robot's (possibly non-linear) dynamics. In addition to contributing a new method for obstacle avoidance, our work contributes to the state-of-the-art in collocation methods for non-linear stochastic optimal control problems in two important ways: (1) we show that taking into account local gradient and second-order derivative information of the optimal value function at the collocation points allows us to exploit knowledge of the derivative information about the system dynamics, and (2) we show that computational savings can be achieved by directly fitting the gradient of the optimal value function rather than the optimal value function itself. We validate our approach on three problems: non-convex obstacle avoidance of a point-mass robot, obstacle avoidance for a 2 degree of freedom robotic manipulator, and optimal control of a non-linear dynamical system.

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IROS Conference 2012 Conference Paper

Minimum angular acceleration control of articulated body dynamics

Javier R. Movellan

As robots find applications in daily life conditions it becomes important to develop controllers that generate energy efficient movements by restricting variability and utilizing high gains only when necessary. Here we present a computationally light and energy efficient approach (AAC) that combines an anticipatory open-loop controller and a variable gain closed loop controller. The approach is grounded in the theory of stochastic optimal control and feedback linearization. As such it links two important approaches to robot control: (1) the family of Computed Torque Controllers (CTC) that are grounded on feedback linearization and classic feedback control, and (2) a more recent family of controllers that aim at finding approximately optimal trade-offs between task performance and energy consumption. Here we show that AAC controllers are highly energy efficient, when compared to CTC, and exhibit some key properties of human motion.

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IROS Conference 2012 Conference Paper

Semi-parametric Gaussian process for robot system identification

Tingfan Wu
Javier R. Movellan

One reason why control of biomimetic robots is so difficult is the fact that we do not have sufficiently accurate mathematical models of their system dynamics. Recent nonparametric machine learning approaches to system identification have shown good promise, outperforming parameterized mathematical models when applied to complex robot system identification problems. Unfortunately, non-parametric methods perform poorly when applied to regions of the state space that are not densely covered by the training dataset. This problem becomes particularly critical as the state space grows. Parametric methods use the available data very efficiently but, on the flip side, they only provide crude approximations to the actual system dynamics. In practice the systematic deviations between the parametric mathematical model and its physical realization results in control laws that do not take advantage of the compliance and complex dynamics of the robot. Here we present an approach to robot system identification, named Semi-Parametric Gaussian Processes (SGP), that elegantly combines the advantages of parametric and non-parametric approaches. Computer simulations and a physical implementation of an underactuated robot system identification problem show very promising results. We also demonstrate the applicability of SGP to articulated tree-structured robots of arbitrary complexity. In all experiments, SGP significantly out-performed previous parametric and non-parametric approaches as well as previous methods for combining the two approaches.

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IROS Conference 2010 Conference Paper

Approaches and databases for online calibration of binaural sound localization for robotic heads

Holger Finger
Shih-Chii Liu
Paul Ruvolo
Javier R. Movellan

In this paper, we evaluate adaptive sound localization algorithms for robotic heads. To this end we built a 3 degree-of-freedom head with two microphones encased in artificial pinnae (outer ears). The geometry of the head and pinnae induce temporal differences in the sound recorded at each microphone. These differences change with the frequency of the sound, location of the sound, and orientation of the robot in a complex manner. To learn the relationship between these auditory differences and the location of a sound source, we applied machine learning methods to a database of different audio source locations and robot head orientations. Our approach achieves a mean error of 2. 5 degrees for azimuth and 11 degrees for elevation for estimating the position of an audio source. The impressive results highlight the benefits of a two-stage regression model to make use of the properties of the artificial pinnae for elevation estimation. In this work, the algorithms were trained using ground truth data provided by a motion capture system. We are currently generalizing the approach so that the training signal is provided online based on a real-time face detection and speech detection system.

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ICRA Conference 2008 Conference Paper

Auditory mood detection for social and educational robots

Paul Ruvolo
Ian R. Fasel
Javier R. Movellan

Social robots face the fundamental challenge of detecting and adapting their behavior to the current social mood. For example, robots that assist teachers in early education must choose different behaviors depending on whether the children are crying, laughing, sleeping, or singing songs. Interactive robotic applications require perceptual algorithms that both run in real time and are adaptable to the challenging conditions of daily life. This paper explores a novel approach to auditory mood detection which was born out of our experience immersing social robots in classroom environments. We propose a new set of low-level spectral contrast features that extends a class of features which have proven very successful for object recognition in the modern computer vision literature. Features are selected and combined using machine learning approaches so as to make decisions about the ongoing auditory mood. We demonstrate excellent performance on two standard emotional speech databases (the Berlin Emotional Speech [W. Burkhardt et al. , 2005], and the ORATOR dataset [H. Quast, 2001]). In addition we establish strong baseline performance for mood detection on a database collected from a social robot immersed in a classroom of 18-24 months old children [J. Movellan er al. , 2007]. This approach operates in real time at little computational cost. It has the potential to greatly enhance the effectiveness of social robots in daily life environments.

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ICRA Conference 2008 Conference Paper

Visual saliency model for robot cameras

Nicholas J. Butko
Lingyun Zhang
Garrison W. Cottrell
Javier R. Movellan

Recent years have seen an explosion of research on the computational modeling of human visual attention in task free conditions, i. e. , given an image predict where humans are likely to look. This area of research could potentially provide general purpose mechanisms for robots to orient their cameras. One difficulty is that most current models of visual saliency are computationally very expensive and not suited to real time implementations needed for robotic applications. Here we propose a fast approximation to a Bayesian model of visual saliency recently proposed in the literature. The approximation can run in real time on current computers at very little computational cost, leaving plenty of CPU cycles for other tasks. We empirically evaluate the saliency model in the domain of controlling saccades of a camera in social robotics situations. The goal was to orient a camera as quickly as possible toward human faces. We found that this simple general purpose saliency model doubled the success rate of the camera: it captured images of people 70% of the time, when compared to a 35% success rate when the camera was controlled using an open-loop scheme. After 3 saccades (camera movements), the robot was 96% likely to capture at least one person. The results suggest that visual saliency models may provide a useful front end for camera control in robotics applications.

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IROS Conference 2004 Conference Paper

Face-to-face interactive humanoid robot

Masahiro Shiomi
Takayuki Kanda 0001
Nicolas Miralles
Takahiro Miyashita
Ian R. Fasel
Javier R. Movellan
Hiroshi Ishiguro

This paper reports progress in the development of a humanoid robot designed for real-time face-to-face interaction with humans. An essential component for face-to-face interaction with humans is being able to find faces, tracking them, and smoothly moving back and forth between gazing to a face and other objects of interest. In this paper we propose a system that integrates peripheral vision and foveal vision in a principled manner using particle filters. The developed system generates hypotheses about face position by using peripheral vision and verifies them by integrating peripheral vision and foveal vision. Even though a face may not be present in foveal vision, while the robot is gazing at another object, it keeps plausible hypotheses about the location of the human face with peripheral vision and restarts the face-following by verifying the hypothesis later. Moreover, this fundamental function provides more rich sensory information for human-robot interaction, such as a human's facial expression and lip motions during utterances.

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