Justus Piater | Empirical Inference - Max Planck Institute for Intelligent Systems

Justus Piater
Research Scientist

Alumni

Empirical Inference

Publications

11 results (BibTeX)

2011

Learning Visual Representations for Interactive Systems

Piater, J., Jodogne, S., Detry, R., Kraft, D., Krüger, N., Kroemer, O., Peters, J.

In Robotics Research, pages: 399-416, (Editors: Pradalier, C. , R. Siegwart, G. Hirzinger), Springer, Berlin, Germany, 14th International Symposium on Robotics Research (ISRR), January 2011 (inproceedings)

Abstract

We describe two quite different methods for associating action parameters to visual percepts. Our RLVC algorithm performs reinforcement learning directly on the visual input space. To make this very large space manageable, RLVC interleaves the reinforcement learner with a supervised classi&amp;#64257;cation algorithm that seeks to split perceptual states so as to reduce perceptual aliasing. This results in an adaptive discretization of the perceptual space based on the presence or absence of visual features. Its extension RLJC also handles continuous action spaces. In contrast to the minimalistic visual representations produced by RLVC and RLJC, our second method learns structural object models for robust object detection and pose estimation by probabilistic inference. To these models, the method associates grasp experiences autonomously learned by trial and error. These experiences form a non-parametric representation of grasp success likelihoods over gripper poses, which we call a gra sp d ensi ty. Thus, object detection in a novel scene simultaneously produces suitable grasping options.

PDF Web DOI [BibTex]

2011

Piater, J., Jodogne, S., Detry, R., Kraft, D., Krüger, N., Kroemer, O., Peters, J. Learning Visual Representations for Interactive Systems In Robotics Research, pages: 399-416, (Editors: Pradalier, C. , R. Siegwart, G. Hirzinger), Springer, Berlin, Germany, 14th International Symposium on Robotics Research (ISRR), January 2011 (inproceedings)

PDF Web DOI [BibTex]

Learning grasp affordance densities

Detry, R., Kraft, D., Kroemer, O., Peters, J., Krüger, N., Piater, J.

Paladyn: Journal of Behavioral Robotics, 2(1):1-17, March 2011 (article)

Abstract

We address the issue of learning and representing object grasp affordance models. We model grasp affordances with continuous probability density functions (grasp densities) which link object-relative grasp poses to their success probability. The underlying function representation is nonparametric and relies on kernel density estimation to provide a continuous model. Grasp densities are learned and refined from exploration, by letting a robot “play” with an object in a sequence of grasp-and-drop actions: the robot uses visual cues to generate a set of grasp hypotheses, which it then executes and records their outcomes. When a satisfactory amount of grasp data is available, an importance-sampling algorithm turns it into a grasp density. We evaluate our method in a largely autonomous learning experiment, run on three objects with distinct shapes. The experiment shows how learning increases success rates. It also measures the success rate of grasps chosen to maximize the probability of success, given reaching constraints.

PDF DOI [BibTex]

Detry, R., Kraft, D., Kroemer, O., Peters, J., Krüger, N., Piater, J. Learning grasp affordance densities Paladyn: Journal of Behavioral Robotics, 2(1):1-17, March 2011 (article)

PDF DOI [BibTex]

Learning Visual Representations for Perception-Action Systems

Piater, J., Jodogne, S., Detry, R., Kraft, D., Krüger, N., Kroemer, O., Peters, J.

International Journal of Robotics Research, 30(3):294-307, February 2011 (article)

Abstract

We discuss vision as a sensory modality for systems that interact flexibly with uncontrolled environments. Instead of trying to build a generic vision system that produces task-independent representations, we argue in favor of task-specific, learnable representations. This concept is illustrated by two examples of our own work. First, our RLVC algorithm performs reinforcement learning directly on the visual input space. To make this very large space manageable, RLVC interleaves the reinforcement learner with a supervised classification algorithm that seeks to split perceptual states so as to reduce perceptual aliasing. This results in an adaptive discretization of the perceptual space based on the presence or absence of visual features. Its extension, RLJC, additionally handles continuous action spaces. In contrast to the minimalistic visual representations produced by RLVC and RLJC, our second method learns structural object models for robust object detection and pose estimation by probabilistic inference. To these models, the method associates grasp experiences autonomously learned by trial and error. These experiences form a non-parametric representation of grasp success likelihoods over gripper poses, which we call a grasp density. Thus, object detection in a novel scene simultaneously produces suitable grasping options.

PDF Web DOI [BibTex]

Piater, J., Jodogne, S., Detry, R., Kraft, D., Krüger, N., Kroemer, O., Peters, J. Learning Visual Representations for Perception-Action Systems International Journal of Robotics Research, 30(3):294-307, February 2011 (article)

PDF Web DOI [BibTex]

2010

Combining active learning and reactive control for robot grasping

Kroemer, O., Detry, R., Piater, J., Peters, J.

Robotics and Autonomous Systems, 58(9):1105-1116, September 2010 (article)

Abstract

Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp requires learning about the object as a whole, the execution only needs to reactively adapt to the context close to the grasps location. We propose a hierarchical controller that reflects the structure of these two sub-problems, and attempts to learn solutions that work for both. A hybrid architecture is employed by the controller to make use of various machine learning methods that can cope with the large amount of uncertainty inherent to the task. The controllers upper level selects where to grasp the object using a reinforcement learner, while the lower level comprises an imitation learner and a vision-based reactive controller to determine appropriate grasping motions. The resulting system is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshapin g the hand depending on the objects geometry. The system was evaluated both in simulation and on a real robot.

PDF Web DOI Project Page [BibTex]

2010

Kroemer, O., Detry, R., Piater, J., Peters, J. Combining active learning and reactive control for robot grasping Robotics and Autonomous Systems, 58(9):1105-1116, September 2010 (article)

PDF Web DOI Project Page [BibTex]

Grasping with Vision Descriptors and Motor Primitives

Kroemer, O., Detry, R., Piater, J., Peters, J.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2010), pages: 47-54, (Editors: Filipe, J. , J. Andrade-Cetto, J.-L. Ferrier), SciTePress , Lisboa, Portugal, 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO), June 2010 (inproceedings)

Abstract

Grasping is one of the most important abilities needed for future service robots. Given the task of picking up an object from betweem clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which may not always be available. Therefore, methods for executing grasps are required, which perform well with information gathered from only standard stereo vision, and make only a few necessary assumptions about the task environment. We propose techniques that reactively modify the robots learned motor primitives based on information derived from Early Cognitive Vision descriptors. The proposed techniques employ non-parametric potential fields centered on the Early Cognitive Vision descriptors to allow for curving hand trajectories around objects, and finger motions that adapt to the objects local geometry. The methods were tested on a real robot and found to allow for easier imitation learning of human movements and give a considerable improvement to the robots performance in grasping tasks.

PDF Web Project Page [BibTex]

Kroemer, O., Detry, R., Piater, J., Peters, J. Grasping with Vision Descriptors and Motor Primitives In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2010), pages: 47-54, (Editors: Filipe, J. , J. Andrade-Cetto, J.-L. Ferrier), SciTePress , Lisboa, Portugal, 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO), June 2010 (inproceedings)

PDF Web Project Page [BibTex]

Learning Continuous Grasp Affordances by Sensorimotor Exploration

Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., Piater, J.

In From Motor Learning to Interaction Learning in Robots, pages: 451-465, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract

We develop means of learning and representing object grasp affordances probabilistically. By grasp affordance, we refer to an entity that is able to assess whether a given relative object-gripper configuration will yield a stable grasp. These affordances are represented with grasp densities, continuous probability density functions defined on the space of 3D positions and orientations. Grasp densities are registered with a visual model of the object they characterize. They are exploited by aligning them to a target object using visual pose estimation. Grasp densities are refined through experience: A robot “plays” with an object by executing grasps drawn randomly for the object’s grasp density. The robot then uses the outcomes of these grasps to build a richer density through an importance sampling mechanism. Initial grasp densities, called hypothesis densities, are bootstrapped from grasps collected using a motion capture system, or from grasps generated from the visual model of the object. Refined densities, called empirical densities, represent affordances that have been confirmed through physical experience. The applicability of our method is demonstrated by producing empirical densities for two object with a real robot and its 3-finger hand. Hypothesis densities are created from visual cues and human demonstration.

PDF Web DOI [BibTex]

Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., Piater, J. Learning Continuous Grasp Affordances by Sensorimotor Exploration In From Motor Learning to Interaction Learning in Robots, pages: 451-465, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

PDF Web DOI [BibTex]

Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision

Erkan, A., Kroemer, O., Detry, R., Altun, Y., Piater, J., Peters, J.

In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pages: 1586-1591, IEEE, Piscataway, NJ, USA, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2010 (inproceedings)

Abstract

This paper addresses the problem of learning and efficiently representing discriminative probabilistic models of object-specific grasp affordances particularly when the number of labeled grasps is extremely limited. The proposed method does not require an explicit 3D model but rather learns an implicit manifold on which it defines a probability distribution over grasp affordances. We obtain hypothetical grasp configurations from visual descriptors that are associated with the contours of an object. While these hypothetical configurations are abundant, labeled configurations are very scarce as these are acquired via time-costly experiments carried out by the robot. Kernel logistic regression (KLR) via joint kernel maps is trained to map the hypothesis space of grasps into continuous class-conditional probability values indicating their achievability. We propose a soft-supervised extension of KLR and a framework to combine the merits of semi-supervised and active learning approaches to tackle the scarcity of labeled grasps. Experimental evaluation shows that combining active and semi-supervised learning is favorable in the existence of an oracle. Furthermore, semi-supervised learning outperforms supervised learning, particularly when the labeled data is very limited.

PDF Web DOI [BibTex]

Erkan, A., Kroemer, O., Detry, R., Altun, Y., Piater, J., Peters, J. Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pages: 1586-1591, IEEE, Piscataway, NJ, USA, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2010 (inproceedings)

PDF Web DOI [BibTex]

Generalizing Demonstrated Actions in Manipulation Tasks

Kroemer, O., Detry, R., Piater, J., Peters, J.

IROS 2010 Workshop on Grasp Planning and Task Learning by Imitation, 2010, pages: 1, October 2010 (poster)

Abstract

Programming-by-demonstration promises to significantly reduce the burden of coding robots to perform new tasks. However, service robots will be presented with a variety of different situations that were not specifically demonstrated to it. In such cases, the robot must autonomously generalize its learned motions to these new situations. We propose a system that can generalize movements to new target locations and even new objects. The former is achieved by using a task-specific coordinate system together with dynamical systems motor primitives. Generalizing actions to new objects is a more complex problem, which we solve by treating it as a continuum-armed bandits problem. Using the bandits framework, we can efficiently optimize the learned action for a specific object. The proposed method was implemented on a real robot and succesfully adapted the grasping action to three different objects. Although we focus on grasping as an example of a task, the proposed methods are much more widely applicable to robot manipulation tasks.

PDF Web [BibTex]

Kroemer, O., Detry, R., Piater, J., Peters, J. Generalizing Demonstrated Actions in Manipulation Tasks IROS 2010 Workshop on Grasp Planning and Task Learning by Imitation, 2010, pages: 1, October 2010 (poster)

PDF Web [BibTex]

Adapting Preshaped Grasping Movements Using Vision Descriptors

Kroemer, O., Detry, R., Piater, J., Peters, J.

In From Animals to Animats 11, pages: 156-166, (Editors: Doncieux, S. , B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, J.-B. Mouret), Springer, Berlin, Germany, 11th International Conference on Simulation of Adaptive Behavior (SAB), August 2010 (inproceedings)

Abstract

Grasping is one of the most important abilities needed for future service robots. In the task of picking up an object from between clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which are often not available. Therefore, methods are needed that execute grasps robustly even with imprecise information gathered only from standard stereo vision. We propose techniques that reactively modify the robot&lsquo;s learned motor primitives based on non-parametric potential fields centered on the Early Cognitive Vision descriptors. These allow both obstacle avoidance, and the adapting of finger motions to the object&lsquo;s local geometry. The methods were tested on a real robot, where they led to improved adaptability and quality of grasping actions.

PDF Web DOI [BibTex]

Kroemer, O., Detry, R., Piater, J., Peters, J. Adapting Preshaped Grasping Movements Using Vision Descriptors In From Animals to Animats 11, pages: 156-166, (Editors: Doncieux, S. , B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, J.-B. Mouret), Springer, Berlin, Germany, 11th International Conference on Simulation of Adaptive Behavior (SAB), August 2010 (inproceedings)

PDF Web DOI [BibTex]

2009

Learning object-specific grasp affordance densities

Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., Piater, J.

In 8th IEEE International Conference on Development and Learning, pages: 1-7, IEEE Service Center, Piscataway, NJ, USA, ICDL, June 2009 (inproceedings)

Abstract

This paper addresses the issue of learning and representing object grasp affordances, i.e. object-gripper relative configurations that lead to successful grasps. The purpose of grasp affordances is to organize and store the whole knowledge that an agent has about the grasping of an object, in order to facilitate reasoning on grasping solutions and their achievability. The affordance representation consists in a continuous probability density function defined on the 6D gripper pose space-3D position and orientation-, within an object-relative reference frame. Grasp affordances are initially learned from various sources, e.g. from imitation or from visual cues, leading to grasp hypothesis densities. Grasp densities are attached to a learned 3D visual object model, and pose estimation of the visual model allows a robotic agent to execute samples from a grasp hypothesis density under various object poses. Grasp outcomes are used to learn grasp empirical densities, i.e. grasps that have been confirmed through experience. We show the result of learning grasp hypothesis densities from both imitation and visual cues, and present grasp empirical densities learned from physical experience by a robot.

PDF Web DOI [BibTex]

2009

Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., Piater, J. Learning object-specific grasp affordance densities In 8th IEEE International Conference on Development and Learning, pages: 1-7, IEEE Service Center, Piscataway, NJ, USA, ICDL, June 2009 (inproceedings)

PDF Web DOI [BibTex]

Active learning using mean shift optimization for robot grasping

Kroemer, O., Detry, R., Piater, J., Peters, J.

In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), pages: 2610-2615, IEEE Service Center, Piscataway, NJ, USA, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2009 (inproceedings)

Abstract

When children learn to grasp a new object, they often know several possible grasping points from observing a parent‘s demonstration and subsequently learn better grasps by trial and error. From a machine learning point of view, this process is an active learning approach. In this paper, we present a new robot learning framework for reproducing this ability in robot grasping. For doing so, we chose a straightforward approach: first, the robot observes a few good grasps by demonstration and learns a value function for these grasps using Gaussian process regression. Subsequently, it chooses grasps which are optimal with respect to this value function using a mean-shift optimization approach, and tries them out on the real system. Upon every completed trial, the value function is updated, and in the following trials it is more likely to choose even better grasping points. This method exhibits fast learning due to the data-efficiency of Gaussian process regression framework and the fact th at t he mean-shift method provides maxima of this cost function. Experiments were repeatedly carried out successfully on a real robot system. After less than sixty trials, our system has adapted its grasping policy to consistently exhibit successful grasps.

PDF Web DOI [BibTex]

Kroemer, O., Detry, R., Piater, J., Peters, J. Active learning using mean shift optimization for robot grasping In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), pages: 2610-2615, IEEE Service Center, Piscataway, NJ, USA, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2009 (inproceedings)

PDF Web DOI [BibTex]

Justus Piater
Research Scientist

2011

Learning Visual Representations for Interactive Systems

2011

Learning grasp affordance densities

Learning Visual Representations for Perception-Action Systems

2010

Combining active learning and reactive control for robot grasping

2010

Grasping with Vision Descriptors and Motor Primitives

Learning Continuous Grasp Affordances by Sensorimotor Exploration

Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision

Generalizing Demonstrated Actions in Manipulation Tasks

Adapting Preshaped Grasping Movements Using Vision Descriptors

2009

Learning object-specific grasp affordance densities

2009

Active learning using mean shift optimization for robot grasping

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Justus Piater Research Scientist

2011

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2010

2009

2009

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Justus Piater
Research Scientist