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2017


Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets

Hausman, K., Chebotar, Y., Schaal, S., Sukhatme, G., Lim, J.

In Proceedings from the conference "Neural Information Processing Systems 2017., (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., Advances in Neural Information Processing Systems 30 (NIPS), December 2017 (inproceedings)

am

pdf video [BibTex]

2017


pdf video [BibTex]


On the Design of {LQR} Kernels for Efficient Controller Learning
On the Design of LQR Kernels for Efficient Controller Learning

Marco, A., Hennig, P., Schaal, S., Trimpe, S.

Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC), pages: 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (conference)

Abstract
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.

am ics pn

arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page [BibTex]

arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page [BibTex]


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Synchronicity Trumps Mischief in Rhythmic Human-Robot Social-Physical Interaction

Fitter, N. T., Kuchenbecker, K. J.

In Proceedings of the International Symposium on Robotics Research (ISRR), Puerto Varas, Chile, December 2017 (inproceedings) In press

Abstract
Hand-clapping games and other forms of rhythmic social-physical interaction might help foster human-robot teamwork, but the design of such interactions has scarcely been explored. We leveraged our prior work to enable the Rethink Robotics Baxter Research Robot to competently play one-handed tempo-matching hand-clapping games with a human user. To understand how such a robot’s capabilities and behaviors affect user perception, we created four versions of this interaction: the hand clapping could be initiated by either the robot or the human, and the non-initiating partner could be either cooperative, yielding synchronous motion, or mischievously uncooperative. Twenty adults tested two clapping tempos in each of these four interaction modes in a random order, rating every trial on standardized scales. The study results showed that having the robot initiate the interaction gave it a more dominant perceived personality. Despite previous results on the intrigue of misbehaving robots, we found that moving synchronously with the robot almost always made the interaction more enjoyable, less mentally taxing, less physically demanding, and lower effort for users than asynchronous interactions caused by robot or human mischief. Taken together, our results indicate that cooperative rhythmic social-physical interaction has the potential to strengthen human-robot partnerships.

hi

[BibTex]

[BibTex]


Optimizing Long-term Predictions for Model-based Policy Search
Optimizing Long-term Predictions for Model-based Policy Search

Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., Trimpe, S.

Proceedings of 1st Annual Conference on Robot Learning (CoRL), 78, pages: 227-238, (Editors: Sergey Levine and Vincent Vanhoucke and Ken Goldberg), 1st Annual Conference on Robot Learning, November 2017 (conference)

Abstract
We propose a novel long-term optimization criterion to improve the robustness of model-based reinforcement learning in real-world scenarios. Learning a dynamics model to derive a solution promises much greater data-efficiency and reusability compared to model-free alternatives. In practice, however, modelbased RL suffers from various imperfections such as noisy input and output data, delays and unmeasured (latent) states. To achieve higher resilience against such effects, we propose to optimize a generative long-term prediction model directly with respect to the likelihood of observed trajectories as opposed to the common approach of optimizing a dynamics model for one-step-ahead predictions. We evaluate the proposed method on several artificial and real-world benchmark problems and compare it to PILCO, a model-based RL framework, in experiments on a manipulation robot. The results show that the proposed method is competitive compared to state-of-the-art model learning methods. In contrast to these more involved models, our model can directly be employed for policy search and outperforms a baseline method in the robot experiment.

am ics

PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Learning optimal gait parameters and impedance profiles for legged locomotion

Heijmink, E., Radulescu, A., Ponton, B., Barasuol, V., Caldwell, D., Semini, C.

Proceedings International Conference on Humanoid Robots, IEEE, 2017 IEEE-RAS 17th International Conference on Humanoid Robots, November 2017 (conference)

Abstract
The successful execution of complex modern robotic tasks often relies on the correct tuning of a large number of parameters. In this paper we present a methodology for improving the performance of a trotting gait by learning the gait parameters, impedance profile and the gains of the control architecture. We show results on a set of terrains, for various speeds using a realistic simulation of a hydraulically actuated system. Our method achieves a reduction in the gait's mechanical energy consumption during locomotion of up to 26%. The simulation results are validated in experimental trials on the hardware system.

am

paper [BibTex]

paper [BibTex]


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A New Data Source for Inverse Dynamics Learning

Kappler, D., Meier, F., Ratliff, N., Schaal, S.

In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2017 (inproceedings)

am

[BibTex]

[BibTex]


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Bayesian Regression for Artifact Correction in Electroencephalography

Fiebig, K., Jayaram, V., Hesse, T., Blank, A., Peters, J., Grosse-Wentrup, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 131-136, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

am ei

DOI [BibTex]

DOI [BibTex]


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Investigating Music Imagery as a Cognitive Paradigm for Low-Cost Brain-Computer Interfaces

Grossberger, L., Hohmann, M. R., Peters, J., Grosse-Wentrup, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 160-164, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

am ei

DOI [BibTex]

DOI [BibTex]


On the relevance of grasp metrics for predicting grasp success
On the relevance of grasp metrics for predicting grasp success

Rubert, C., Kappler, D., Morales, A., Schaal, S., Bohg, J.

In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots and Systems, September 2017 (inproceedings) Accepted

Abstract
We aim to reliably predict whether a grasp on a known object is successful before it is executed in the real world. There is an entire suite of grasp metrics that has already been developed which rely on precisely known contact points between object and hand. However, it remains unclear whether and how they may be combined into a general purpose grasp stability predictor. In this paper, we analyze these questions by leveraging a large scale database of simulated grasps on a wide variety of objects. For each grasp, we compute the value of seven metrics. Each grasp is annotated by human subjects with ground truth stability labels. Given this data set, we train several classification methods to find out whether there is some underlying, non-trivial structure in the data that is difficult to model manually but can be learned. Quantitative and qualitative results show the complexity of the prediction problem. We found that a good prediction performance critically depends on using a combination of metrics as input features. Furthermore, non-parametric and non-linear classifiers best capture the structure in the data.

am

Project Page [BibTex]

Project Page [BibTex]


An Interactive Robotic System for Promoting Social Engagement
An Interactive Robotic System for Promoting Social Engagement

Burns, R., Javed, H., Jeon, M., Howard, A., Park, C. H.

In International Conference on Intelligent Robots and Systems (IROS) 2017, International Conference on Intelligent Robots and Systems, September 2017 (inproceedings)

Abstract
This abstract (and poster) is a condensed version of Burns' Master's thesis and related journal article. It discusses the use of imitation via robotic motion learning to improve human-robot interaction. It focuses on the preliminary results from a pilot study of 12 subjects. We hypothesized that the robot's use of imitation will increase the user's openness towards engaging with the robot. Post-imitation, experimental subjects displayed a more positive emotional state, had higher instances of mood contagion towards the robot, and interpreted the robot to have a higher level of autonomy than their control group counterparts. These results point to an increased user interest in engagement fueled by personalized imitation during interaction.

hi

[BibTex]

[BibTex]


A Robotic Framework to Overcome Sensory Overload in Children on the Autism Spectrum: A Pilot Study
A Robotic Framework to Overcome Sensory Overload in Children on the Autism Spectrum: A Pilot Study

Javed, H., Burns, R., Jeon, M., Howard, A., Park, C. H.

In International Conference on Intelligent Robots and Systems (IROS) 2017, International Conference on Intelligent Robots and Systems, September 2017 (inproceedings)

Abstract
This paper discusses a novel framework designed to provide sensory stimulation to children with Autism Spectrum Disorder (ASD). The set up consists of multi-sensory stations to stimulate visual/auditory/olfactory/gustatory/tactile/vestibular senses, together with a robotic agent that navigates through each station responding to the different stimuli. We hypothesize that the robot’s responses will help children learn acceptable ways to respond to stimuli that might otherwise trigger sensory overload. Preliminary results from a pilot study conducted to examine the effectiveness of such a setup were encouraging and are described briefly in this text.

hi

[BibTex]

[BibTex]


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Local Bayesian Optimization of Motor Skills

Akrour, R., Sorokin, D., Peters, J., Neumann, G.

Proceedings of the 34th International Conference on Machine Learning, 70, pages: 41-50, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (conference)

am ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

Chebotar, Y., Hausman, K., Zhang, M., Sukhatme, G., Schaal, S., Levine, S.

Proceedings of the 34th International Conference on Machine Learning, 70, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (conference)

am

pdf video [BibTex]

pdf video [BibTex]


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Stiffness Perception during Pinching and Dissection with Teleoperated Haptic Forceps

Ng, C., Zareinia, K., Sun, Q., Kuchenbecker, K. J.

In Proceedings of the International Symposium on Robot and Human Interactive Communication (RO-MAN), pages: 456-463, Lisbon, Portugal, August 2017 (inproceedings)

hi

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Design of a Parallel Continuum Manipulator for 6-DOF Fingertip Haptic Display

Young, E. M., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 599-604, Munich, Germany, June 2017, Finalist for best poster paper (inproceedings)

Abstract
Despite rapid advancements in the field of fingertip haptics, rendering tactile cues with six degrees of freedom (6 DOF) remains an elusive challenge. In this paper, we investigate the potential of displaying fingertip haptic sensations with a 6-DOF parallel continuum manipulator (PCM) that mounts to the user's index finger and moves a contact platform around the fingertip. Compared to traditional mechanisms composed of rigid links and discrete joints, PCMs have the potential to be strong, dexterous, and compact, but they are also more complicated to design. We define the design space of 6-DOF parallel continuum manipulators and outline a process for refining such a device for fingertip haptic applications. Following extensive simulation, we obtain 12 designs that meet our specifications, construct a manually actuated prototype of one such design, and evaluate the simulation's ability to accurately predict the prototype's motion. Finally, we demonstrate the range of deliverable fingertip tactile cues, including a normal force into the finger and shear forces tangent to the finger at three extreme points on the boundary of the fingertip.

hi

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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High Magnitude Unidirectional Haptic Force Display Using a Motor/Brake Pair and a Cable

Hu, S., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 394-399, Munich, Germany, June 2017 (inproceedings)

Abstract
Clever electromechanical design is required to make the force feedback delivered by a kinesthetic haptic interface both strong and safe. This paper explores a onedimensional haptic force display that combines a DC motor and a magnetic particle brake on the same shaft. Rather than a rigid linkage, a spooled cable connects the user to the actuators to enable a large workspace, reduce the moving mass, and eliminate the sticky residual force from the brake. This design combines the high torque/power ratio of the brake and the active output capabilities of the motor to provide a wider range of forces than can be achieved with either actuator alone. A prototype of this device was built, its performance was characterized, and it was used to simulate constant force sources and virtual springs and dampers. Compared to the conventional design of using only a motor, the hybrid device can output higher unidirectional forces at the expense of free space feeling less free.

hi

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A Wrist-Squeezing Force-Feedback System for Robotic Surgery Training

Brown, J. D., Fernandez, J. N., Cohen, S. P., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 107-112, Munich, Germany, June 2017 (inproceedings)

Abstract
Over time, surgical trainees learn to compensate for the lack of haptic feedback in commercial robotic minimally invasive surgical systems. Incorporating touch cues into robotic surgery training could potentially shorten this learning process if the benefits of haptic feedback were sustained after it is removed. In this paper, we develop a wrist-squeezing haptic feedback system and evaluate whether it holds the potential to train novice da Vinci users to reduce the force they exert on a bimanual inanimate training task. Subjects were randomly divided into two groups according to a multiple baseline experimental design. Each of the ten participants moved a ring along a curved wire nine times while the haptic feedback was conditionally withheld, provided, and withheld again. The realtime tactile feedback of applied force magnitude significantly reduced the integral of the force produced by the da Vinci tools on the task materials, and this result remained even when the haptic feedback was removed. Overall, our findings suggest that wrist-squeezing force feedback can play an essential role in helping novice trainees learn to minimize the force they exert with a surgical robot.

hi

DOI [BibTex]

DOI [BibTex]


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Handling Scan-Time Parameters in Haptic Surface Classification

Burka, A., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 424-429, Munich, Germany, June 2017 (inproceedings)

hi

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

Doerr, A., Nguyen-Tuong, D., Marco, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 5295-5301, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

am ics

PDF arXiv DOI Project Page [BibTex]

PDF arXiv DOI Project Page [BibTex]


Learning Feedback Terms for Reactive Planning and Control
Learning Feedback Terms for Reactive Planning and Control

Rai, A., Sutanto, G., Schaal, S., Meier, F.

Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (conference)

am

pdf video [BibTex]

pdf video [BibTex]


Virtual vs. {R}eal: Trading Off Simulations and Physical Experiments in Reinforcement Learning with {B}ayesian Optimization
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization

Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 1557-1563, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

am ics pn

PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]

PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]


Path Integral Guided Policy Search
Path Integral Guided Policy Search

Chebotar, Y., Kalakrishnan, M., Yahya, A., Li, A., Schaal, S., Levine, S.

Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (conference)

am

pdf video [BibTex]

pdf video [BibTex]


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Proton 2: Increasing the Sensitivity and Portability of a Visuo-haptic Surface Interaction Recorder

Burka, A., Rajvanshi, A., Allen, S., Kuchenbecker, K. J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 439-445, Singapore, May 2017 (inproceedings)

Abstract
The Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short) is a new handheld visuo-haptic sensing system that records surface interactions. We previously demonstrated system calibration and a classification task using external motion tracking. This paper details improvements in surface classification performance and removal of the dependence on external motion tracking, necessary before embarking on our goal of gathering a vast surface interaction dataset. Two experiments were performed to refine data collection parameters. After adjusting the placement and filtering of the Proton's high-bandwidth accelerometers, we recorded interactions between two differently-sized steel tooling ball end-effectors (diameter 6.35 and 9.525 mm) and five surfaces. Using features based on normal force, tangential force, end-effector speed, and contact vibration, we trained multi-class SVMs to classify the surfaces using 50 ms chunks of data from each end-effector. Classification accuracies of 84.5% and 91.5% respectively were achieved on unseen test data, an improvement over prior results. In parallel, we pursued on-board motion tracking, using the Proton's camera and fiducial markers. Motion tracks from the external and onboard trackers agree within 2 mm and 0.01 rad RMS, and the accuracy decreases only slightly to 87.7% when using onboard tracking for the 9.525 mm end-effector. These experiments indicate that the Proton 2 is ready for portable data collection.

hi

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Robot Therapist for Assisting in At-Home Rehabilitation of Shoulder Surgery Patients
Robot Therapist for Assisting in At-Home Rehabilitation of Shoulder Surgery Patients

(Recipient of Innovation & Entrepreneurship Prize)

Burns, R., Alborz, M., Chalup, Z., Downen, S., Genuino, K., Nayback, C., Nesbitt, N., Park, C. H.

In 2017 GW Research Days, Department of Biomedical Engineering Posters and Presentations, April 2017 (inproceedings)

Abstract
The number of middle-aged to elderly patients receiving shoulder surgery is increasing. However, statistically, very few of these patients perform the necessary at-home physical therapy regimen they are prescribed post-surgery. This results in longer recovery times and/or incomplete healing. We propose the use of a robotic therapist, with customized training and encouragement regimens, to increase physical therapy adherence and improve the patient’s recovery experience.

hi

link (url) [BibTex]

link (url) [BibTex]


Motion Learning for Emotional Interaction and Imitation of Children with Autism Spectrum Disorder
Motion Learning for Emotional Interaction and Imitation of Children with Autism Spectrum Disorder

(First place tie in category, "Biomedical Engineering, Graduate Research")

Burns, R., Cowin, S.

In 2017 GW Research Days, Department of Biomedical Engineering Posters and Presentations, April 2017 (inproceedings)

Abstract
We aim to use motion learning to teach a robot to imitate people's unique gestures. Our robot, ROBOTIS-OP2, can ultimately use imitation to practice social skills with children with autism. In this abstract, two methods of motion learning were compared: Dynamic motion primitives with least squares (DMP with WLS), and Dynamic motion primitives with a Gaussian Mixture Regression (DMP with GMR). Movements with sharp turns were most accurately reproduced using DMP with GMR. Additionally, more states are required to accurately recreate more complex gestures.

hi

link (url) [BibTex]

link (url) [BibTex]


Roughness perception of virtual textures displayed by electrovibration on touch screens
Roughness perception of virtual textures displayed by electrovibration on touch screens

Vardar, Y., Isleyen, A., Saleem, M. K., Basdogan, C.

In 2017 IEEE World Haptics Conference (WHC), pages: 263-268, 2017 (inproceedings)

Abstract
In this study, we have investigated the human roughness perception of periodical textures on an electrostatic display by conducting psychophysical experiments with 10 subjects. To generate virtual textures, we used low frequency unipolar pulse waves in different waveform (sinusoidal, square, saw-tooth, triangle), and spacing. We modulated these waves with a 3kHz high frequency sinusoidal carrier signal to minimize perceptional differences due to the electrical filtering of human finger and eliminate low-frequency distortions. The subjects were asked to rate 40 different macro textures on a Likert scale of 1-7. We also collected the normal and tangential forces acting on the fingers of subjects during the experiment. The results of our user study showed that subjects perceived the square wave as the roughest while they perceived the other waveforms equally rough. The perceived roughness followed an inverted U-shaped curve as a function of groove width, but the peak point shifted to the left compared to the results of the earlier studies. Moreover, we found that the roughness perception of subjects is best correlated with the rate of change of the contact forces rather than themselves.

hi

vardar_whc2017 DOI [BibTex]

vardar_whc2017 DOI [BibTex]


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Feeling multiple edges: The tactile perception of short ultrasonic square reductions of the finger-surface friction

Gueorguiev, D., Vezzoli, E., Sednaoui, T., Grisoni, L., Lemaire-Semail, B.

In 2017 IEEE World Haptics Conference (WHC), pages: 125-129, 2017 (inproceedings)

hi

DOI [BibTex]

DOI [BibTex]

2013


Probabilistic Object Tracking Using a Range Camera
Probabilistic Object Tracking Using a Range Camera

Wüthrich, M., Pastor, P., Kalakrishnan, M., Bohg, J., Schaal, S.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 3195-3202, IEEE, November 2013 (inproceedings)

Abstract
We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compute a posterior distribution over the current object pose. Depending on whether a robot or a human manipulates the object, we employ a process model with or without knowledge of control inputs. Observations are obtained from a range camera. As opposed to previous object tracking methods, we explicitly model self-occlusions and occlusions from the environment, e.g, the human or robotic hand. This leads to a strongly non-linear observation model and additional dependencies in the Bayesian network. We employ a Rao-Blackwellised particle filter to compute an estimate of the object pose at every time step. In a set of experiments, we demonstrate the ability of our method to accurately and robustly track the object pose in real-time while it is being manipulated by a human or a robot.

am

arXiv Video Code Video DOI Project Page [BibTex]

2013


arXiv Video Code Video DOI Project Page [BibTex]


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Virtual Robotization of the Human Body via Data-Driven Vibrotactile Feedback

Kurihara, Y., Hachisu, T., Kuchenbecker, K. J., Kajimoto, H.

In Proc. International Conference on Advances in Computer Entertainment Technology (ACE), 8253, pages: 109-122, Lecture Notes in Computer Science, Springer, Enschede, Netherlands, 2013, Oral presentation given by Kurihara. Best Paper Silver Award (inproceedings)

hi

[BibTex]

[BibTex]


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Virtual Robotization of the Human Body Using Vibration Recording, Modeling and Rendering

Kurihara, Y., Hachisu, T., Kuchenbecker, K. J., Kajimoto, H.

In Proc. Virtual Reality Society of Japan Annual Conference, Osaka, Japan, sep 2013, Paper written in Japanese. Presentation given by Kurihara (inproceedings)

hi

[BibTex]

[BibTex]


Hypothesis Testing Framework for Active Object Detection
Hypothesis Testing Framework for Active Object Detection

Sankaran, B., Atanasov, N., Le Ny, J., Koletschka, T., Pappas, G., Daniilidis, K.

In IEEE International Conference on Robotics and Automation (ICRA), May 2013, clmc (inproceedings)

Abstract
One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of view-points, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active M-ary hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and experiments with real scenes captured by a kinect sensor. The results suggest a significant improvement over static object detection.

am

pdf [BibTex]

pdf [BibTex]


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Virtual Alteration of Body Material by Reality-Based Periodic Vibrotactile Feedback

Kurihara, Y., Hachisu, T., Sato, M., Fukushima, S., Kuchenbecker, K. J., Kajimoto, H.

In Proc. JSME Robotics and Mechatronics Conference (ROBOMEC), Tsukuba, Japan, May 2013, Paper written in Japanese. Poster presentation given by {Kurihara} (inproceedings)

hi

[BibTex]

[BibTex]


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The Design and Field Observation of a Haptic Notification System for Oral Presentations

Tam, D., MacLean, K. E., McGrenere, J., Kuchenbecker, K. J.

In Proc. SIGCHI Conference on Human Factors in Computing Systems, pages: 1689-1698, Paris, France, May 2013, Oral presentation given by Tam (inproceedings)

hi

[BibTex]

[BibTex]


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Using Robotic Exploratory Procedures to Learn the Meaning of Haptic Adjectives

Chu, V., McMahon, I., Riano, L., McDonald, C. G., He, Q., Perez-Tejada, J. M., Arrigo, M., Fitter, N., Nappo, J., Darrell, T., Kuchenbecker, K. J.

In Proc. IEEE International Conference on Robotics and Automation, pages: 3048-3055, Karlsruhe, Germany, May 2013, Oral presentation given by Chu. Best Cognitive Robotics Paper Award (inproceedings)

hi

[BibTex]

[BibTex]


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Instrument contact vibrations are a construct-valid measure of technical skill in Fundamentals of Laparoscopic Surgery Training Tasks

Gomez, E. D., Aggarwal, R., McMahan, W., Koch, E., Hashimoto, D. A., Darzi, A., Murayama, K. M., Dumon, K. R., Williams, N. N., Kuchenbecker, K. J.

In Proc. Annual Meeting of the Association for Surgical Education, Orlando, Florida, USA, 2013, Oral presentation given by Gomez (inproceedings)

hi

[BibTex]

[BibTex]


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Dynamic Simulation of Tool-Mediated Texture Interaction

McDonald, C. G., Kuchenbecker, K. J.

In Proc. IEEE World Haptics Conference, pages: 307-312, Daejeon, South Korea, April 2013, Oral presentation given by McDonald (inproceedings)

hi

[BibTex]

[BibTex]


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Generating Haptic Texture Models From Unconstrained Tool-Surface Interactions

Culbertson, H., Unwin, J., Goodman, B. E., Kuchenbecker, K. J.

In Proc. IEEE World Haptics Conference, pages: 295-300, Daejeon, South Korea, April 2013, Oral presentation given by Culbertson. Finalist for Best Paper Award (inproceedings)

hi

[BibTex]

[BibTex]


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A practical System for Recording Instrument Contacts and Collisions During Transoral Robotic Surgery

Gomez, E. D., Weinstein, G. S., O’Malley, J. B. W., McMahan, W., Chen, L., Kuchenbecker, K. J.

In Proc. Annual Meeting of the Triological Society, Orlando, Florida, USA, April 2013, Poster presentation given by Gomez (inproceedings)

hi

[BibTex]

[BibTex]


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Action and Goal Related Decision Variables Modulate the Competition Between Multiple Potential Targets

Enachescu, V, Christopoulos, Vassilios N, Schrater, P. R., Schaal, S.

In Abstracts of Neural Control of Movement Conference (NCM 2013), February 2013 (inproceedings)

am

[BibTex]

[BibTex]


The functional role of automatic body response in shaping voluntary actions based on muscle synergy theory
The functional role of automatic body response in shaping voluntary actions based on muscle synergy theory

Alnajjar, F. S., Berenz, V., Shimoda, S.

In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, pages: 1230-1233, 2013 (inproceedings)

am

DOI [BibTex]

DOI [BibTex]


Coaching robots with biosignals based on human affective social behaviors
Coaching robots with biosignals based on human affective social behaviors

Suzuki, K., Gruebler, A., Berenz, V.

In ACM/IEEE International Conference on Human-Robot Interaction, HRI 2013, Tokyo, Japan, March 3-6, 2013, pages: 419-420, 2013 (inproceedings)

am

link (url) [BibTex]

link (url) [BibTex]


Fusing visual and tactile sensing for 3-D object reconstruction while grasping
Fusing visual and tactile sensing for 3-D object reconstruction while grasping

Ilonen, J., Bohg, J., Kyrki, V.

In IEEE International Conference on Robotics and Automation (ICRA), pages: 3547-3554, 2013 (inproceedings)

Abstract
In this work, we propose to reconstruct a complete 3-D model of an unknown object by fusion of visual and tactile information while the object is grasped. Assuming the object is symmetric, a first hypothesis of its complete 3-D shape is generated from a single view. This initial model is used to plan a grasp on the object which is then executed with a robotic manipulator equipped with tactile sensors. Given the detected contacts between the fingers and the object, the full object model including the symmetry parameters can be refined. This refined model will then allow the planning of more complex manipulation tasks. The main contribution of this work is an optimal estimation approach for the fusion of visual and tactile data applying the constraint of object symmetry. The fusion is formulated as a state estimation problem and solved with an iterative extended Kalman filter. The approach is validated experimentally using both artificial and real data from two different robotic platforms.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


Self-tuning in Sliding Mode Control of High-Precision Motion Systems
Self-tuning in Sliding Mode Control of High-Precision Motion Systems

Heertjes, M. F., Vardar, Y.

In IFAC Proceedings Volumes, 46(5):13 - 19, 2013, 6th IFAC Symposium on Mechatronic Systems (inproceedings)

Abstract
In high-precision motion systems, set-point tracking often comes with the problem of overshoot, hence poor settling behavior. To avoid overshoot, PD control (thus without using an integrator) is preferred over PID control. However, PD control gives rise to steady-state error in view of the constant disturbances acting on the system. To deal with both overshoot and steady-state error, a sliding mode controller with saturated integrator is studied. For large servo signals the controller is switched to PD mode as to constrain the integrator buffer and therefore the overshoot. For small servo signals the controller switches to PID mode as to avoid steady-state error. The tuning of the switching parameters will be done automatically with the aim to optimize the settling behavior. The sliding mode controller will be tested on a high-precision motion system.

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heertjes_ifac2013 link (url) DOI [BibTex]

heertjes_ifac2013 link (url) DOI [BibTex]


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Learning Objective Functions for Manipulation

Kalakrishnan, M., Pastor, P., Righetti, L., Schaal, S.

In 2013 IEEE International Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, 2013 (inproceedings)

Abstract
We present an approach to learning objective functions for robotic manipulation based on inverse reinforcement learning. Our path integral inverse reinforcement learning algorithm can deal with high-dimensional continuous state-action spaces, and only requires local optimality of demonstrated trajectories. We use L 1 regularization in order to achieve feature selection, and propose an efficient algorithm to minimize the resulting convex objective function. We demonstrate our approach by applying it to two core problems in robotic manipulation. First, we learn a cost function for redundancy resolution in inverse kinematics. Second, we use our method to learn a cost function over trajectories, which is then used in optimization-based motion planning for grasping and manipulation tasks. Experimental results show that our method outperforms previous algorithms in high-dimensional settings.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Practical System For Recording Instrument Interactions During Live Robotic Surgery

McMahan, W., Gomez, E. D., Chen, L., Bark, K., Nappo, J. C., Koch, E. I., Lee, D. I., Dumon, K., Williams, N., Kuchenbecker, K. J.

In Proc. Medicine Meets Virtual Reality, 2013, Poster presentation given by McMahan (inproceedings)

hi

[BibTex]

[BibTex]


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Learning Task Error Models for Manipulation

Pastor, P., Kalakrishnan, M., Binney, J., Kelly, J., Righetti, L., Sukhatme, G. S., Schaal, S.

In 2013 IEEE Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, 2013 (inproceedings)

Abstract
Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipulation tasks, especially when visual servoing is rendered infeasible due to occlusions. A lot of research has been conducted to estimate geometric and non-geometric parameters of kinematic chains to minimize reconstruction errors. However, kinematic chains can include non-linearities, e.g. due to cable stretch and motor-side encoders, that result in significantly different errors for different parts of the state space. Previous work either does not consider such non-linearities or proposes to estimate non-geometric parameters of carefully engineered models that are robot specific. We propose a data-driven approach that learns task error models that account for such unmodeled non-linearities. We argue that in the context of grasping and manipulation, it is sufficient to achieve high accuracy in the task relevant state space. We identify this relevant state space using previously executed joint configurations and learn error corrections for those. Therefore, our system is developed to generate subsequent executions that are similar to previous ones. The experiments show that our method successfully captures the non-linearities in the head kinematic chain (due to a counterbalancing spring) and the arm kinematic chains (due to cable stretch) of the considered experimental platform, see Fig. 1. The feasibility of the presented error learning approach has also been evaluated in independent DARPA ARM-S testing contributing to successfully complete 67 out of 72 grasping and manipulation tasks.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2006


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Improving Telerobotic Touch Via High-Frequency Acceleration Matching

Kuchenbecker, K. J., Niemeyer, G.

In Proc. IEEE International Conference on Robotics and Automation, pages: 3893-3898, Orlando, Florida, USA, May 2006, Oral presentation given by Kuchenbecker (inproceedings)

hi

[BibTex]

2006


[BibTex]


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Event-Based Haptic Tapping with Grip Force Compensation

Fiene, J. P., Kuchenbecker, K. J., Niemeyer, G.

In Proc. IEEE Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pages: 117-123, Arlington, Virginia, USA, March 2006, Oral presentation given by Fiene (inproceedings)

hi

[BibTex]

[BibTex]


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Learning operational space control

Peters, J., Schaal, S.

In Robotics: Science and Systems II (RSS 2006), pages: 255-262, (Editors: Gaurav S. Sukhatme and Stefan Schaal and Wolfram Burgard and Dieter Fox), Cambridge, MA: MIT Press, RSS , 2006, clmc (inproceedings)

Abstract
While operational space control is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in complex robots, e.g., humanoid robots. In such cases, learning control methods can offer an interesting alternative to analytical control algorithms. However, the resulting learning problem is ill-defined as it requires to learn an inverse mapping of a usually redundant system, which is well known to suffer from the property of non-covexity of the solution space, i.e., the learning system could generate motor commands that try to steer the robot into physically impossible configurations. A first important insight for this paper is that, nevertheless, a physically correct solution to the inverse problem does exits when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on a recent insight that many operational space controllers can be understood in terms of a constraint optimal control problem. The cost function associated with this optimal control problem allows us to formulate a learning algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the view of machine learning, the learning problem corresponds to a reinforcement learning problem that maximizes an immediate reward and that employs an expectation-maximization policy search algorithm. Evaluations on a three degrees of freedom robot arm illustrate the feasability of our suggested approach.

am ei

link (url) [BibTex]

link (url) [BibTex]


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Reinforcement Learning for Parameterized Motor Primitives

Peters, J., Schaal, S.

In Proceedings of the 2006 International Joint Conference on Neural Networks, pages: 73-80, IJCNN, 2006, clmc (inproceedings)

Abstract
One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the "building blocks of movement generation", called motor primitives. Motor primitives, as used in this paper, are parameterized control policies such as splines or nonlinear differential equations with desired attractor properties. While a lot of progress has been made in teaching parameterized motor primitives using supervised or imitation learning, the self-improvement by interaction of the system with the environment remains a challenging problem. In this paper, we evaluate different reinforcement learning approaches for improving the performance of parameterized motor primitives. For pursuing this goal, we highlight the difficulties with current reinforcement learning methods, and outline both established and novel algorithms for the gradient-based improvement of parameterized policies. We compare these algorithms in the context of motor primitive learning, and show that our most modern algorithm, the Episodic Natural Actor-Critic outperforms previous algorithms by at least an order of magnitude. We demonstrate the efficiency of this reinforcement learning method in the application of learning to hit a baseball with an anthropomorphic robot arm.

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]