1. Challenges Bayesian Estimation for Autonomous Object Manipulation Based on Tactile Perception Anna Petrovskaya, Oussama Khatib, Sebastian Thrun, Andrew Y. NgSTANFORD Related WorkOur Contributions positions torques force sensing Motivation Today robots operate in factory assembly lines, where all parameters are known in advance. To move robots into home/office environment these parameters have to be estimated from sensory information. A lot of sensory information is being generated within the robot itself: joint positions, torques and force sensor readings. Humans use this type of information to sense the environment. We would like robots to do the same. Exploration Model Run Sampling Algorithm Shrink Search Space to High Likelihood Region Increase Precision e.g. importance sampling or particle filter Traditionally manipulation work does not have probabilistic basis. However, probabilistic approach has proven to be robust in handling uncertainty in mobile robotics. While there is very little work on probabilistic tactile perception, a few groups have recently approached the problem. In 2004, variants of Kalman filters were used to estimate parameters during cube-in-corner assembly task by P. Slaets et al. In 2003, histogram filter was used for haptic mapping by M. Schaeffer and A. M. Okamura. In 2001, object localization in 3 DoF using particle filters was performed by K. Gadeyne and H. Bruyninckx. Similarly to object localization considered in prior art, we estimate object’s position from manipulator data. In contrast to prior art, we localize the object in 6 DoF. Our focus is on developing a method that is independent of the object itself. It turns out that generalizing from 3 DoF to 6 DoF is computationally non-trivial and requires better algorithms. Goals: 6 DoF localization 0.5 m starting uncertainty 1 mm precision of estimation Real time operation Measurements: 1Measurements: 2Measurements: 3 Here the robot explores an object by touching it. Each point represents a possible guess of where initial interaction with the object took place. The correct location is denoted by red oval. As the robot collects more measurements, it reduces the possibilities. However, during the first few interactions the belief is highly multi-modal. MethodStrengthsChallengesKalman filtersEfficient in high dimensions Not well suited for highly multi-modal problems Optimization search Efficient in high dimensions Number of local minima increases with object complexity Gridding or uniform sampling Well suited for multi- modal problems Computational complexity exponential in number of parameters We focus on sampling methods. Thus efficiency is the main challenge. For example, if a gridding search with 1mm precision requires 1 million samples and takes 1 second in 3 DoF, in 6 DoF it will require approximately 24 days. Objects are modeled as poly-mesh Each measurement includes contact point and surface normal: (c x, c y, c z, n x, n y, n z ) Y = (c x, c y, c z, n x, n y, n z ) Samples (i.e. “guesses of state”) include position and orientation of the object:Samples (i.e. “guesses of state”) include position and orientation of the object: X = (x, y, z, α, β, γ) Importance weights areImportance weights are p({Y i }| X) Uniform Sampling (0.5m) 3 x (360˚) 3 10 15 Sampling uniformly in (0.5m) 3 x (360˚) 3 with precision of 1mm and 1˚ requires 10 15 samples. Instead we consider using “broad samples”, that represent regions of space. To do so, we blur measurement model in proportion to region radius. From Coarse to Fine Resolution It turns out that no single fixed radius of “broad samples” works well. However, if we start with a large radius and gradually shrink it, then we get good results. Scaling Series Algorithm The approach can be summarized as follows: Opening Doors The mobile platform is teleoperated, but door handle probing and operation is completely autonomous. Real Environments Part of STanford AI Robot (STAIR) project Part of STanford AI Robot (STAIR) project Long term goals for STAIR: Long term goals for STAIR: Tidy up after party Tidy up after party Show guests around Show guests around Assemble a bookshelf Assemble a bookshelf First task: First task: Open a door Open a door Results Task: locate and grasp rectangular box with PUMA Task: locate and grasp rectangular box with PUMA 1 second running time 1 second running time 100% success on 21 real datasets 100% success on 21 real datasets 99.8% success on 1000 simulated runs 99.8% success on 1000 simulated runs