Alumni
Stefan Schaal is Professor of Computer Science, Neuroscience, and Biomedical Engineering at the University of Southern California. He is a Founding Director of the Max-Planck-Insitute for Intelligent Systems in Germany where he led the Autonomous Motion Department for several years. He was also an Invited Researcher at the ATR Computational Neuroscience Laboratory in Japan, where he held an appointment as Head of the Computational Learning Group during an international ERATO project, the Kawato Dynamic Brain Project (ERATO/JST). Before joining USC, Dr. Schaal was a postdoctoral fellow at the Department of Brain and Cognitive Sciences and the Artificial Intelligence Laboratory at MIT, an Invited Researcher at the ATR Human Information Processing Research Laboratories in Japan, and an Adjunct Assistant Professor at the Georgia Institute of Technology and at the Department of Kinesiology of the Pennsylvania State University.
Dr. Schaal's research interests include topics of statistical and machine learning, neural networks, computational neuroscience, functional brain imaging, nonlinear dynamics, nonlinear control theory, and biomimetic robotics. He applies his research to problems of artificial and biological motor control and motor learning, focusing on both theoretical investigations and experiments with human subjects and anthropomorphic robot equipment.
Dr. Schaal has co-authored over 400 papers in refereed journals and conferences. He is a co-founder of the "IEEE/RAS International Conference and Humanoid Robotics", and a co-founder of "Robotics Science and Systems", a highly selective new conference featuring the best work in robotics every year. Dr. Schaal served as Program Chair at these conferences and he was the Program Chair of "Simulated and Adaptive Behavior" (SAB 2004) and the "IEEE/RAS International Conference on Robotics and Automation" (ICRA 2008), the largest robotics conference in the world. Dr. Schaal is has also been an Area Chair at "Neural Information Processing Systems" (NIPS) and served as Program Committee Member of the "International Conference on Machine Learning" (ICML). Dr. Schaal serves on the editorial board of the journals "Neural Networks", "International Journal of Humanoid Robotics", and "Frontiers in Neurorobotics". Dr. Schaal is a member of the German National Academic Foundation (Studienstiftung des Deutschen Volkes), the Alexander von Humboldt Foundation, the Society For Neuroscience, the Society for Neural Control of Movement, the IEEE, and AAAS.
artificial intelligence robotics computational neuroscience machine learning
One challenge towards autonomous manipulation is to cope with uncertainty and noise in the sensory-motor system of the robot and the environment. We argue that such a system requires to close feedback control loops in novel ways, using predictive models and leveraging previous experiences.
Manipulation tasks often decompos...
Stefan Schaal Manuel Wüthrich Peter Pastor Mrinal Kalakrishnan Franzi Meier Daniel Kappler
We have developed algorithms which enable an autonomous manipulation system to grasp a wide range of objects and to perform a certain number of manipulation tasks, such as drilling, using a stapler, unlocking a door with a key or changing a tire \cite{Righetti_AR_2013}. More generally, we are interested in providing complete integra...
Ludovic Righetti Mrinal Kalakrishnan Peter Pastor Manuel Wüthrich Alexander Herzog Jeannette Bohg Stefan Schaal
Autonomous systems such as humanoid robots are characterized by a multitude of feedback control loops operating at different hierarchical levels and time-scales. Designing and tuning these controllers typically requires significant manual modeling and design effort and exhaustive experimental testing. For managing the ever greater c...
Alonso Marco Valle Sebastian Trimpe Philipp Hennig Alexander von Rohr Jeannette Bohg Stefan Schaal
Decision making requires knowledge of some variables of interest. In the vast majority of real-world problems, these variables are latent, i.e. they cannot be observed directly and must be inferred from available measurements. To maintain an up-to-date distribution over the latent variables, past beliefs have to ...
Manuel Wüthrich Sebastian Trimpe Cristina Garcia Cifuentes Jan Issac Daniel Kappler Franzi Meier Jeannette Bohg Stefan Schaal
Besides accuracy and sample efficiency, computational cost is a crucial design criterion for machine learning algorithms in real-time settings, such as control problems. An example is the modeling of robot dynamics: The sensors in a robot can produce thousands of data points per second, quickly amassing a coverage of the task relate...
There is compelling evidence that perception in humans and animals is an active and exploratory process. For example, Gibson showed that physical interaction further augments perceptual processing beyond what can be achieved by just looking at the environment. In the specific experiment, human subjects had to find a reference object...
Tuning and designing robotic behavior by combining elementary objective terms is a tedious task which generally consists of finding proper representations for each new skill. Inverse Optimal Control (IOC) allows, by specifying a set of basis functions (or features), to learn the right association of objective terms defining a policy...
Jim Mainprice Mrinal Kalakrishnan Peter Pastor Nathan Ratliff Ludovic Righetti Stefan Schaal Andreas Doerr
In order to perform well in day to day tasks, humanoid robots need to be able to adapt to the changes in their work environments. In response to a change in the environment, the robot can pursue at least two different strategies: First, it could re-plan, a process that is often computational...
In Reinforcement Learning (RL), an agent strives to learn a task solely by interacting with an unknown environment. Given the agent’s inputs to the environment and the observed outputs, model-based RL algorithms make efficient use of all available data by constructing a model of the un...
Data-driven methods towards grasping address the challenges of grasp synthesis that arise in the real world such as noisy sensors and incomplete information about the objects and the environment. They focus on finding a suitable representation of the perceptual data that allows to predict whether a certain grasp will succeed.
...Proportional, Integral and Derivative (PID) control architectures cover a significant portion of today’s industrial control applications. The PID control law for a Single-Input Single-Output (SISO) system is given by
\begin{equation}<...
Andreas Doerr Sebastian Trimpe Alonso Marco Valle Stefan Schaal
Motion generation is increasingly formalized as a large scale optimization over future outcomes of actions. For high dimensional manipulation platforms, this optimization is computationally so difficult that for a long time traditional approaches focused primarily on feasibility of the solution rather than even local optimality. Rec...
Nathan Ratliff Ludovic Righetti Jim Mainprice Jeannette Bohg Stefan Schaal
An important part of our research is concerned with the problem of movement representations. The way motion and contacts are represented is crucial to derive efficient planning and control algorithms, for example by significantly simplifying the underlying optimization problems. The way sensory information can be integrated to gen...
Ludovic Righetti Stefan Schaal Peter Pastor Mrinal Kalakrishnan
Planning dynamic behaviors for legged robots is a challenging task because the robot is subject to strong dynamic constraints due to its floating base (i.e. it can fall). It needs to take into account intermittent contacts with the environment and apply contact forces in order to move.
In this project, we address the probl...
Ludovic Righetti Alexander Herzog Nick Rotella Sean Mason Felix Grimminger John Rebula Brahayam Ponton Stefan Schaal
Hand-eye coordination is crucial for capable manipulation of objects. It requires to know the manipulator's and the objects' locations. These locations have to be inferred from sensory data. In this project we work with range sensors, which are wide spread in robotics and provide dense depth images.
...Manuel Wüthrich Jan Issac Cristina Garcia Cifuentes Jeannette Bohg Claudia Pfreundt Peter Pastor Mrinal Kalakrishnan Daniel Kappler Sebastian Trimpe Franzi Meier Stefan Schaal
We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. Our approach emphasizes the importance of continuous, real-time perception and its tight integration with reactive motion genera...
Jeannette Bohg Daniel Kappler Franzi Meier Jan Issac Jim Mainprice Cristina Garcia Cifuentes Manuel Wüthrich Vincent Berenz Stefan Schaal Nathan Ratliff
Purposeful and robust manipulation requires a good hand-eye coordination. To a certain extend this can be achieved using information from joint encoders and known kinematics. However, for many robots a significant error in the pose of the end-effector and fingers of several centimeters remains. Especially for fine manipulation tasks...
Jeannette Bohg Alexander Herzog Stefan Schaal Javier Romero Felix Widmaier
In this project, we explore the problem of fusing sensor information from inertial, position and force measurements to recover quantities fundamental for the feedback control of legged robots. Our final goal is to find a systematic way of fusing multiple sensor modalities to improve the control of legged robots.
When developing controllers for legged robots, one assumes that important quantities like the Center of Mass of the robot (CoM), its position and orientation in space or its joint positions and velocities are know accurately to be used in feedback laws. While the estimation of such quantities is trivial in simulation, it becomes a s...
Nick Rotella Sean Mason Alexander Herzog Ludovic Righetti Stefan Schaal
Autonomous robotic grasping is one of the pre-requisites for personal robots to become useful when assisting humans in households. Seamlessly easy for humans, it still remains a very challenging task for robots. The key problem of robotic grasping is to automatically choose an appropriate grasp configuration given an object as perce...
Alexander Herzog Peter Pastor Mrinal Kalakrishnan Ludovic Righetti Jeannette Bohg Stefan Schaal
Legged robots are expected to locomote autonomously in an uncertain and potentially dynamically changing environment. Active interaction with contacts becomes inevitable to move and apply forces in a goal-directed way and withstand unpredicted changes in the environment. Therefore, we need to design algorithms that exploit interacti...
Ludovic Righetti Alexander Herzog Nick Rotella Sean Mason Felix Grimminger Stefan Schaal
am
Berenz, V., Schaal, S.
Playful: Reactive Programming for Orchestrating Robotic Behavior
IEEE Robotics Automation Magazine, 25(3):49-60, September 2018 (article) In press
am
ics
Doerr, A., Daniel, C., Schiegg, M., Nguyen-Tuong, D., Schaal, S., Toussaint, M., Trimpe, S.
Probabilistic Recurrent State-Space Models
In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), July 2018 (inproceedings) Accepted
am
Kappler, D., Meier, F., Issac, J., Mainprice, J., Garcia Cifuentes, C., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.
Real-time Perception meets Reactive Motion Generation
IEEE Robotics and Automation Letters, 3(3):1864-1871, July 2018 (article)
am
Meier, F., Kappler, D., Schaal, S.
Online Learning of a Memory for Learning Rates
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018, accepted (inproceedings)
am
Sutanto, G., Su, Z., Schaal, S., Meier, F.
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)
am
Kloss, A., Schaal, S., Bohg, J.
Combining learned and analytical models for predicting action effects
arXiv, 2018 (article) Submitted
am
mg
Ponton, B., Herzog, A., Del Prete, A., Schaal, S., Righetti, L.
On Time Optimization of Centroidal Momentum Dynamics
In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 5776-5782, IEEE, Brisbane, Australia, May 2018 (inproceedings)
am
mg
Rotella, N., Schaal, S., Righetti, L.
Unsupervised Contact Learning for Humanoid Estimation and Control
In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 411-417, IEEE, Brisbane, Australia, 2018 (inproceedings)
am
mg
Gams, A., Mason, S., Ude, A., Schaal, S., Righetti, L.
Learning Task-Specific Dynamics to Improve Whole-Body Control
In Hua, IEEE, Beijing, China, November 2018 (inproceedings)
am
mg
Mason, S., Rotella, N., Schaal, S., Righetti, L.
An MPC Walking Framework With External Contact Forces
In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1785-1790, IEEE, Brisbane, Australia, May 2018 (inproceedings)
am
Hausman, K., Chebotar, Y., Schaal, S., Sukhatme, G., Lim, J.
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
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
ics
pn
Marco, A., Hennig, P., Schaal, S., Trimpe, S.
On the Design of LQR Kernels for Efficient Controller Learning
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)
am
Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., Sukhatme, G.
Interactive Perception: Leveraging Action in Perception and Perception in Action
IEEE Transactions on Robotics, 33, pages: 1273-1291, December 2017 (article)
am
ics
Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., Trimpe, S.
Optimizing Long-term Predictions for Model-based Policy Search
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) Accepted
am
Kappler, D., Meier, F., Ratliff, N., Schaal, S.
A New Data Source for Inverse Dynamics Learning
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
Rubert, C., Kappler, D., Morales, A., Schaal, S., Bohg, J.
On the relevance of grasp metrics for predicting grasp success
In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots and Systems, September 2017 (inproceedings) Accepted
am
Chebotar, Y., Hausman, K., Zhang, M., Sukhatme, G., Schaal, S., Levine, S.
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
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
ics
Doerr, A., Nguyen-Tuong, D., Marco, A., Schaal, S., Trimpe, S.
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
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
Rai, A., Sutanto, G., Schaal, S., Meier, F.
Learning Feedback Terms for Reactive Planning and Control
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
ics
pn
Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S.
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization
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
Chebotar, Y., Kalakrishnan, M., Yahya, A., Li, A., Schaal, S., Levine, S.
Path Integral Guided Policy Search
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
Garcia Cifuentes, C., Issac, J., Wüthrich, M., Schaal, S., Bohg, J.
Probabilistic Articulated Real-Time Tracking for Robot Manipulation
IEEE Robotics and Automation Letters (RA-L), 2(2):577-584, April 2017 (article)
am
ei
Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J., Schaal, S.
Robot Learning
In Springer Handbook of Robotics, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)
am
ics
Wüthrich, M., Trimpe, S., Garcia Cifuentes, C., Kappler, D., Schaal, S.
A New Perspective and Extension of the Gaussian Filter
The International Journal of Robotics Research, 35(14):1731-1749, December 2016 (article)
am
The Role of Measurement Uncertainty in Optimal Control for Contact Interactions
Workshop on the Algorithmic Foundations of Robotics, pages: 22, November 2016 (conference)
am
Kloss, A., Kappler, D., Lensch, H. P. A., Butz, M. V., Schaal, S., Bohg, J.
Learning Where to Search Using Visual Attention
Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference)
am
Bohg, J., Kappler, D., Meier, F., Ratliff, N., Mainprice, J., Issac, J., Wüthrich, M., Garcia Cifuentes, C., Berenz, V., Schaal, S.
Interlocking Perception-Action Loops at Multiple Time Scales - A System Proposal for Manipulation in Uncertain and Dynamic Environments
In International Workshop on Robotics in the 21st century: Challenges and Promises, September 2016 (inproceedings)
am
Ratliff, N., Meier, F., Kappler, D., Schaal, S.
DOOMED: Direct Online Optimization of Modeling Errors in Dynamics
arXiv preprint arXiv:1608.00309, August 2016 (article)
am
ics
Wüthrich, M., Garcia Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S.
Robust Gaussian Filtering using a Pseudo Measurement
In Proceedings of the American Control Conference (ACC), Boston, MA, USA, July 2016 (inproceedings)
am
Widmaier, F., Kappler, D., Schaal, S., Bohg, J.
Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
Kappler, D., Schaal, S., Bohg, J.
Optimizing for what matters: the Top Grasp Hypothesis
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
Bohg, J., Kappler, D., Schaal, S.
Exemplar-based Prediction of Object Properties from Local Shape Similarity
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
ics
pn
Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.
Automatic LQR Tuning Based on Gaussian Process Global Optimization
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 270-277, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
ics
Issac, J., Wüthrich, M., Garcia Cifuentes, C., Bohg, J., Trimpe, S., Schaal, S.
Depth-based Object Tracking Using a Robust Gaussian Filter
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
Meier, F., Schaal, S.
Drifting Gaussian Processes with Varying Neighborhood Sizes for Online Model Learning
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
Oh, Y., Sutanto, G., Mistry, M., Schweighofer, N., Schaal, S.
Distinct adaptation to abrupt and gradual torque perturbations with a multi-joint exoskeleton robot
Abstracts of Neural Control of Movement Conference (NCM 2016), Montego Bay, Jamaica, April 2016 (poster)
am
Meier, F., Kappler, D., Ratliff, N., Schaal, S.
Towards Robust Online Inverse Dynamics Learning
Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, 2016 (conference) Accepted
am
Chebotar, Y., Hausman, K., Su, Z., Sukhatme, G., Schaal, S.
Self-Supervised Regrasping using Spatio-Temporal Tactile Features and Reinforcement Learning
In International Conference on Intelligent Robots and Systems (IROS) 2016, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016 (inproceedings)
am
Ting, J., Meier, F., Vijayakumar, S., Schaal, S.
Locally Weighted Regression for Control
In Encyclopedia of Machine Learning and Data Mining, pages: 1-14, Springer US, Boston, MA, 2016 (inbook)
am
Chebotar, Y., Hausman, K., Kroemer, O., Sukhatme, G., Schaal, S.
Generalizing Regrasping with Supervised Policy Learning
In International Symposium on Experimental Robotics (ISER) 2016, International Symposium on Experimental Robotics, 2016 (inproceedings)
am
mg
Herzog, A., Rotella, N., Mason, S., Grimminger, F., Schaal, S., Righetti, L.
Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid
Autonomous Robots, 40(3):473-491, 2016 (article)
am
mg
Rotella, N., Mason, S., Schaal, S., Righetti, L.
Inertial Sensor-Based Humanoid Joint State Estimation
In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages: 1825-1831, IEEE, Stockholm, Sweden, 2016 (inproceedings)
am
mg
Khadiv, M., Kleff, S., Herzog, A., Moosavian, S. A. A., Schaal, S., Righetti, L.
Stepping Stabilization Using a Combination of DCM Tracking and Step Adjustment
In 2016 4th International Conference on Robotics and Mechatronics (ICROM), pages: 130-135, IEEE, Teheran, Iran, 2016 (inproceedings)
am
mg
Herzog, A., Schaal, S., Righetti, L.
Structured contact force optimization for kino-dynamic motion generation
In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 2703-2710, IEEE, Daejeon, South Korea, 2016 (inproceedings)
am
mg
Mason, S., Rotella, N., Schaal, S., Righetti, L.
Balancing and Walking Using Full Dynamics LQR Control With Contact Constraints
In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 63-68, IEEE, Cancun, Mexico, 2016 (inproceedings)
am
mg
Ponton, B., Schaal, S., Righetti, L.
On the Effects of Measurement Uncertainty in Optimal Control of Contact Interactions
In The 12th International Workshop on the Algorithmic Foundations of Robotics WAFR, Berkeley, USA, 2016 (inproceedings)
am
mg
Ponton, B., Herzog, A., Schaal, S., Righetti, L.
A Convex Model of Momentum Dynamics for Multi-Contact Motion Generation
In 2016 IEEE-RAS 16th International Conference on Humanoid Robots Humanoids, pages: 842-849, IEEE, Cancun, Mexico, 2016 (inproceedings)
am
ei
ics
pn
Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.
Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results
Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)
am
ics
Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., Schaal, S.
Direct Loss Minimization Inverse Optimal Control
In Proceedings of Robotics: Science and Systems, Rome, Italy, Robotics: Science and Systems XI, July 2015 (inproceedings)
am
Kappler, D., Bohg, B., Schaal, S.
Leveraging Big Data for Grasp Planning
In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)