Publications

Many publications can be found on Google Scholar. Among the most relevant publications are the following:

2017

Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., & Sukhatme, G. (2017). Interactive Perception: Leveraging Action in Perception and Perception in Action. IEEE Transactions on Robotics, 33, 1273–1291.

Chebotar, Y., Hausman, K., Su, Z., Sukhatme, G. S., & Schaal, S. (2016). Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning (pp. 1960–1966). Presented at the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. doi:10.1109/IROS.2016.7759309

Chebotar, Y., Hausman, K., Zhang, M., Sukhatme, G., Schaal, S., & Levine, S. (2017). Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. Presented at the International Conference on Machine Learning (ICML) 2017.

Chebotar, Y., Kalakrishnan, M., Yahya, A., Li, A., Schaal, S., & Levine, S. (2017). Path Integral Guided Policy Search. Presented at the Proceedings of the IEEE International Conference on Robotics and Automation.

Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., & Trimpe, S. (2017). Optimizing Long-term Predictions for Model-based Policy Search (Vol. 78, pp. 227–238). Presented at the Proceedings of Machine Learning Research.

2014

Floreano, D., Ijspeert, A. J., & Schaal, S. (2014). Robotics and Neuroscience. Current Biology, 24(18), R910–R920. doi:10.1016/j.cub.2014.07.058

Garcia Cifuentes, C., Issac, J., Wuthrich, M., Schaal, S., & Bohg, J. (2017). Probabilistic Articulated Real-Time Tracking for Robot Manipulation. IEEE Robotics and Automation Letters (RA-L), 2(2), 577–584.

Hausman, K., Chebotar, Y., Schaal, S., Sukhatme, G., & Lim, J. (2017). Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets. Presented at the Proceedings Neural Information Processing Systems.

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., & Schaal, S. (2014). Learning of grasp selection based on shape-templates. Auton. Robots, 36(1-2), 51–65. doi:10.1007/s10514-013-9366-8

Herzog, A., Rotella, N., Mason, S., Grimminger, F., Schaal, S., & Righetti, L. (2016). Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid. Autonomous Robots, 40(3), 473–491. doi:10.1162/NECO_a_00393

Kappler, D., Bohg, J., & Schaal, S. (2015). Leveraging big data for grasp planning, 4304–4311. doi:10.1109/ICRA.2015.7139793

Kappler, D., Schaal, S., & Bohg, J. (2016). Optimizing for what matters: the Top Grasp Hypothesis. Presented at the Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE.

Mason, S., Rotella, N., Schaal, S., & Righetti, L. (2016). Balancing and Walking Using Full Dynamics LQR Control with Contact Constraints. Presented at the Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots.

Meier, F., & Schaal, S. (2016). Drifting Gaussian Processes with Varying Neighborhood Sizes for Online Model Learning. Presented at the Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE.

Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J. A., & Schaal, S. (2015). Robot Learning. In Springer Handbook of Robotics 2nd Edition (pp. 1371–1394). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-540-30301-5_60

Ratliff, N., Toussaint, M., & Schaal, S. (2015). Understanding the Geometry of Workspace Obstacles in Motion Optimization. Presented at the Proceedings of the IEEE International Conference on Robotics and Automation.

Righetti, L., Kalakrishnan, M., Pastor, P., Binney, J., Kelly, J., Voorhies, R. C., et al. (2014). An autonomous manipulation system based on force control and optimization. Auton. Robots, 36(1-2), 11–30. doi:10.1007/s10514-013-9365-9

Rotella, N., Bloesch, M., Righetti, L., & Schaal, S. (2014, February 21). State Estimation for a Humanoid Robot. http://arxiv.org/abs/1402.5450. Accessed 1 August 2014

Rotella, N., Herzog, A., Schaal, S., & Righetti, L. (2015). Momentum Estimation, Planning and Control for Force-Centric Bipedal Locomotion. Presented at the Proceedings of Dynamic Walking.

Schaal, S. (2015). Autonomous Robots. In Jahrbuch der Max-Planck-Gesellschaft.

Spatz, J. P., & Schaal, S. (2014). Perspective: Intelligent Systems: Bits and Bots. Nature, (509). http://www-clmc.usc.edu/publications/S/spatz-Nature2014.pdf

Su, Z., Hausman, K., Chebotar, Y., Molchanov, A., Loeb, G. E., Sukhatme, G. S., & Schaal, S. (2015). Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor (pp. 297–303). Presented at the IEEE-RAS International Conference on Humanoid Robots (Humanoids).

Ting, J.-A., Meier, F., Vijayakumar, S., & Schaal, S. (2016). Locally Weighted Regression for Control. In Encyclopedia of Machine Learning and Data Mining (pp. 1–14). Boston, MA: Springer US. doi:10.1007/978-1-4899-7502-7_493-1

Vitiello, N., Ijspeert, A. J., & Schaal, S. (2016). Bioinspired Motor Control for Articulated Robots. IEEE Robotics & Automation Magazine, 23(1), 20–21. doi:10.1109/MRA.2015.2511680

Wuthrich, M., Trimpe, S., Garcia Cifuentes, C., Kappler, D., & Schaal, S. (2016). A New Perspective and Extension of the Gaussian Filter. The International Journal of Robotics Research, 35(14), 1731–1749.

Bohg, J., Romero, J., Herzog, A., & Schaal, S. (2014). Robot Arm Pose Estimation through Pixel-Wise Part Classification. Presented at the Proceedings of the IEEE International Conference on Robotics and Automation, HongKong, PC. http://www-clmc.usc.edu/publications/B/bogh-ICRA2014.pdf

Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., & Schaal, S. (2013). Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors. Neural Computation, 25(2), 328–373. doi:10.1162/NECO_a_00393

Kalakrishnan, M., Buchli, J., Pastor, P., Mistry, M., & Schaal, S. (2011). Learning, planning, and control for quadruped locomotion over challenging terrain. International Journal of Robotics Research, 30(2), 236–258. http://www-clmc.usc.edu/publications/K/kalakrishnan-IJRR2011.pdf

Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., & Schaal, S. (2011). STOMP: Stochastic trajectory optimization for motion planning (pp. 4569–4574). Presented at the Robotics and Automation (ICRA), 2011 IEEE International Conference on. doi:10.1109/ICRA.2011.5980280

Kalakrishnan, M., Righetti, L., Pastor, P., & Schaal, S. (2012). Learning Force Control Policies for Compliant Robotic Manipulation. Presented at the International Conference on Machine Learning, Edinburgh.

Meier, F., Theodorou, E., & Schaal, S. (2012). Movement Segmentation and Recognition for Imitation Learning. International Conference on Artificial Intelligence and Statistics (AISTATS). http://jmlr.csail.mit.edu/proceedings/papers/v22/meier12/meier12.pdf

Mistry, M., Theodorou, E., Schaal, S., & Kawato, M. (2013). Optimal control of reaching includes kinematic constraints. Journal of Neurophysiology, 110(1), 1–11. doi:10.1152/jn.00794.2011

Pastor, P., Kalakrishnan, M., Binney, J., Kelly, J., Righetti, L., Sukhatme, G., & Schaal, S. (2013). Learning task error models for manipulation (pp. 2612–2618). Presented at the Robotics and Automation (ICRA), 2013 IEEE International Conference on. doi:10.1109/ICRA.2013.6630935

Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., & Schaal, S. (2011). Skill learning and task outcome prediction for manipulation (pp. 3828–3834). Presented at the Robotics and Automation (ICRA), 2011 IEEE International Conference on. doi:10.1109/ICRA.2011.5980200

Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J., Theodorou, E., & Schaal, S. (2013). From dynamic movement primitives to associative skill memories. Robotics and Autonomous Systems, 61(4), 351–361. doi:10.1016/j.robot.2012.09.017

Rai, A., Meier, F., Ijspeert, A., & Schaal, S. (2014). Learning coupling terms for obstacle avoidance (pp. 512–518). Presented at the International Conference on Humanoid Robotics, IEEE. doi:10.1109/HUMANOIDS.2014.7041410

Righetti, L., & Schaal, S. (2012). Quadratic Programming for Inverse Dynamics with Optimal Distribution of Contact Forces (pp. 538–543). Presented at the IEEE-RAS International Conference on Humanoid Robots, Osaka.

Righetti, L., Buchli, J., Mistry, M., Kalakrishnan, M., & Schaal, S. (2012). Optimal distribution of contact forces with inverse dynamics control. International Journal of Robotics Research.

Righetti, L., Buchli, J., Mistry, M., Kalakrishnan, M., & Schaal, S. (2013). Optimal distribution of contact forces with inverse-dynamics control. The International Journal of Robotics Research, 32(3), 280–298. doi:10.1177/0278364912469821

Stulp, F., Buchli, J., Ellmer, A., Mistry, M., Theodorou, E. A., & Schaal, S. (2012). Model-free reinforcement learning of impedance control in stochastic environments. Autonomous Mental Development, IEEE Transactions on, 4(4), 330–341.

Stulp, F., Theodorou, E., & Schaal, S. (2012). Reinforcement Learning with Sequences of Motion Primitives for Robust Manipulation. IEEE Transactions on Robotics.

Ting, J.-A., D’Souza, A., & Schaal, S. (2011). Bayesian robot system identification with input and output noise. Neural Networks, 24(1). http://www-clmc.usc.edu/publications/T/ting-NN2011.pdf

Wuthrich, M., Pastor, P., Kalakrishnan, M., Bohg, J., & Schaal, S. (2013). Probabilistic object tracking using a range camera (pp. 3195–3202). Presented at the Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on. doi:10.1109/IROS.2013.6696810

Billard, A., Calinon, S., Dillmann, R., & Schaal, S. (2008). Robot Programming by Demonstration. In B. Siciliano & O. Khatib (Eds.), Handbook of Robotics (Vol. 1). MIT Press.

Buchli, J., Stulp, F., Theodorou, E., & Schaal, S. (2011). Learning variable impedance control (Vol. 30, pp. 820–833). Presented at the International Journal of Robotics Research. doi:10.1177/0278364911402527

Buchli, J., Theodorou, E., Stulp, F., & Schaal, S. (2010). Variable impedance control – a reinforcement learning approach (pp. 1–8). Presented at the Robotics Science and Systems (2010), Zaragoza, Spain, June 27-30.

Cheng, G., Hyon, S.-H., Ude, A., Morimoto, J., Hale, J. G., Hart, J., et al. (2008). CB: Exploring neuroscience with a humanoid research platform. Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, 1772–1773.

Gams, A., Ijspeert, A. J., Schaal, S., & Lenarčič, J. (2009). On-line learning and modulation of periodic movements with nonlinear dynamical systems. Autonomous Robots, 27(1), 3–23. doi:10.1007/s10514-009-9118-y

Hoffman, H., Schaal, S., & Vijayakumar, S. (2009). Local dimensionality reduction for non-parametric regression. Neural Processing Letters, 29(2), 109–131. doi:10.1007/s11063-009-9098-0

Hoffmann, H., Pastor, P., & Schaal, S. (2008). Dynamic movement primitives for movement generation motivated by convergent force fields in frog. Presented at the Adaptive Motion of Animals and Machines (AMAM), Cleveland, Ohio, 2008.

Hoffmann, H., Schaal, S., & Vijayakumar, S. (2009). Local Dimensionality Reduction for Non-Parametric Regression. Neural Processing Letters, 29(2), 109–131. doi:10.1007/s11063-009-9098-0

Hoffmann, H., Theodorou, E., & Schaal, S. (2009). Human optimization strategies under reward feedback. Presented at the Abstracts of Neural Control of Movement Conference (NCM 2009), Waikoloa, Hawaii, 2009.

Kalakrishnan, M., Buchli, J., Pastor, P., Mistry, M., & Schaal, S. (2010). Fast, robust quadruped locomotion over challenging terrain (pp. 2665–2670). Presented at the Robotics and Automation, 2007 IEEE International Conference on.

Klanke, S., Vijayakumar, S., & Schaal, S. (2008). A library for locally weighted projection regression. Journal of Machine Learning Research, 9, 623–626.

Loeb, G. E., Tsianos, G. A., Fishel, J. A., Wettels, N., & Schaal, S. (2011). Understanding haptics by evolving mechatronic systems. Progress in Brain Research, 192, 129. doi:10.1016/B978-0-444-53355-5.00009-9

Meier, F., Theodorou, E., Stulp, F., & Schaal, S. (2011). Movement segmentation using a primitive library (pp. 3407–3412). Presented at the Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on. doi:10.1109/IROS.2011.6094676

Mistry, M., Buchli, J., & Schaal, S. (2010). Inverse dynamics control of floating base systems using orthogonal decomposition (pp. 3406–3412). Presented at the Robotics and Automation, 2007 IEEE International Conference on. doi:10.1109/ROBOT.2010.5509646

Nakanishi, J., Cory, R., Mistry, M., Peters, J., & Schaal, S. (2008a). Operational Space Control: A Theoretical and Empirical Comparison. The International Journal of Robotics Research, 27(6), 737–757. doi:10.1177/0278364908091463

Nakanishi, J., Cory, R., Mistry, M., Peters, J., & Schaal, S. (2008b). Operational space control: A theoretical and emprical comparison. International Journal of Robotics Research, 27, 737–757.

Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S. (2009). Learning and generalization of motor skills by learning from demonstration (pp. 763–768). Presented at the Robotics and Automation, 2009. ICRA ’09. IEEE International Conference on, Kobe, Japan, May 12-19, 2009. doi:10.1109/ROBOT.2009.5152385

Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., & Schaal, S. (2011). Skill Learning and Task Outcome Prediction for Manipulation. IEEE International Conference on Robotics and Automation, 3828–3834.

Peters, J., & Schaal, S. (2008a). Reinforcement learning of motor skills with policy gradients. Neural Netw, 21(4), 682–697.

Peters, J., & Schaal, S. (2008b). Learning to Control in Operational Space. The International Journal of Robotics Research, 27(2), 197–212. doi:10.1177/0278364907087548

Peters, J., & Schaal, S. (2008c). Natural actor critic. Neurocomputing, 71(7-9), 1180–1190.

Peters, J., & Schaal, S. (2008d). Learning to control in operational space. International Journal of Robotics Research, 27, 197–212.

Peters, J., Mistry, M., Udwadia, F. E., Nakanishi, J., & Schaal, S. (2008). A unifying methodology for robot control with redundant DOFs. Autonomous Robots, 24(1), 1–12.

Peters, J., Mistry, M., Udwadia, F., Nakanishi, J., & Schaal, S. (2007). A unifying framework for robot control with redundant DOFs. Autonomous Robots, 24(1), 1–12. doi:10.1007/s10514-007-9051-x

Righetti, L., Buchli, J., Mistry, M., & Schaal, S. (2011). Inverse dynamics control of floating-base robots with external constraints: A unified view (pp. 1085–1090). Presented at the Robotics and Automation (ICRA), 2011 IEEE International Conference on. doi:10.1109/ICRA.2011.5980156

Schaal, S. (2007a). The new robotics—towards human‐centered machines. HFSP Journal, 1(2), 115–126. doi:10.2976/1.2748612

Schaal, S. (2007b). The computational neurobiology of reaching and pointing – a foundation for motor learning: By Reza Shadmehr and Steven P. Wise. Network: Computation in Neural Systems, 18(1), 1–3.

Schaal, S. (2009). The SL simulation and real-time control software package. Los Angeles, CA: University of Southern California.

Schaal, S., & Atkeson, C. G. (2010). Learning control in robotics — trajectory-based opitimal control techniques. Robotics and Automation Magazine, 17(2), 20–29.

Stulp, F., & Schaal, S. (2011). Hierarchical reinforcement learning with movement primitives (pp. 231–238). Presented at the Humanoid Robots (Humanoids), 2011 11th IEEE-RAS International Conference on. doi:10.1109/Humanoids.2011.6100841

Stulp, F., Theodorou, E., Buchli, J., & Schaal, S. (2011). Learning to grasp under uncertainty (pp. 5703–5708). Presented at the Robotics and Automation (ICRA), 2011 IEEE International Conference on. doi:10.1109/ICRA.2011.5979644

Ting, J., D’Souza, A., Vijayakumar, S., & Schaal, S. (2010). Efficient learning and feature detection in high dimensional regression. Neural Computation, 22, 831–886.

Ting, J.-A., D’Souza, A., Vijayakumar, S., & Schaal, S. (2008). A Bayesian approach to empirical local linearizations for robotics (pp. 2860–2865). Presented at the International Conference on Robotics and Automation (ICRA2008), Pasadena, CA, USA, May 19-23, 2008.

Ting, J.-A., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., et al. (2008). Variational Bayesian least squares: An application to brain–machine interface data. Neural Networks, 21(8), 1112–1131. doi:10.1016/j.neunet.2008.06.012

Ting, J.-A., Vijayakumar, S., & Schaal, S. (2010). Locally weighted regression for control. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 613–624). Springer.