Unscented Bayesian optimization for safe robot grasping

Safe and robust grasping of unknown objects is a major challenge in robotics, which has no general solution yet. A promising approach relies on haptic exploration, where active optimization strategies can be employed to reduce the number of exploration trials. One critical problem is that certain optimal grasps discoverd by the optimization procedure may be very sensitive to small deviations of the parameters from their nominal values: we call these unsafe grasps because small errors during motor execution may turn optimal grasps into bad grasps. To reduce the risk of grasp failure, safe grasps should be favoured. Therefore, we propose a new algorithm, unscented Bayesian optimization, that performs efficient optimization while considering uncertainty in the input space, leading to the discovery of safe optima. The results highlight how our method outperforms the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations, finding robust and safe grasps after a few exploration trials.

[1]  Howie Choset,et al.  Adapting control policies for expensive systems to changing environments , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Adam D. Bull,et al.  Convergence Rates of Efficient Global Optimization Algorithms , 2011, J. Mach. Learn. Res..

[3]  Robert B. Gramacy,et al.  Cases for the nugget in modeling computer experiments , 2010, Statistics and Computing.

[4]  Andreas Krause,et al.  Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.

[5]  Oliver Kroemer,et al.  Active reward learning with a novel acquisition function , 2015, Auton. Robots.

[6]  Wolfram Burgard,et al.  Active Policy Learning for Robot Planning and Exploration under Uncertainty , 2008 .

[7]  Jan Peters,et al.  Bayesian optimization for learning gaits under uncertainty , 2015, Annals of Mathematics and Artificial Intelligence.

[8]  Jonas Mockus,et al.  Application of Bayesian approach to numerical methods of global and stochastic optimization , 1994, J. Glob. Optim..

[9]  Nando de Freitas,et al.  Active Policy Learning for Robot Planning and Exploration under Uncertainty , 2007, Robotics: Science and Systems.

[10]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[11]  Aaron Klein,et al.  Efficient and Robust Automated Machine Learning , 2015, NIPS.

[12]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[13]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[14]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[15]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[16]  Oliver Kroemer,et al.  Combining active learning and reactive control for robot grasping , 2010, Robotics Auton. Syst..

[17]  Nando de Freitas,et al.  A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot , 2009, Auton. Robots.

[18]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[19]  Abdeslam Boularias,et al.  Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects , 2014, AAAI.

[20]  Andreas Krause,et al.  Safe controller optimization for quadrotors with Gaussian processes , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Andy J. Keane,et al.  Optimization using surrogate models and partially converged computational fluid dynamics simulations , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[22]  Benoît Chachuat,et al.  Bayesian Optimization with Dimension Scheduling: Application to Biological Systems , 2015, ArXiv.

[23]  R JonesDonald,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998 .

[24]  Niels Kjølstad Poulsen,et al.  New developments in state estimation for nonlinear systems , 2000, Autom..

[25]  N. Zheng,et al.  Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models , 2006, J. Glob. Optim..

[26]  Oliver Kroemer,et al.  Active learning using mean shift optimization for robot grasping , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Fabio Tozeto Ramos,et al.  Bayesian Optimisation for informative continuous path planning , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[28]  Kirthevasan Kandasamy,et al.  High Dimensional Bayesian Optimisation and Bandits via Additive Models , 2015, ICML.

[29]  Carl E. Rasmussen,et al.  Gaussian Process Training with Input Noise , 2011, NIPS.

[30]  Simon J. Julier,et al.  The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[31]  Nando de Freitas,et al.  A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.

[32]  Ruben Martinez-Cantin Locally-Biased Bayesian Optimization using Nonstationary Gaussian Processes , 2015 .

[33]  Honglak Lee,et al.  Deep learning for detecting robotic grasps , 2013, Int. J. Robotics Res..

[34]  Herbert K. H. Lee,et al.  Bayesian Guided Pattern Search for Robust Local Optimization , 2009, Technometrics.

[35]  Ruben Martinez-Cantin,et al.  BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits , 2014, J. Mach. Learn. Res..

[36]  Antoine Cully,et al.  Robots that can adapt like animals , 2014, Nature.

[37]  Nando de Freitas,et al.  Heteroscedastic Treed Bayesian Optimisation , 2014, ArXiv.

[38]  Roman Garnett,et al.  Bayesian optimization for sensor set selection , 2010, IPSN '10.