S T A N F O R D. Method. Strengths. Challenges. Kalman filters. Gridding or uniform sampling. Optimization search. Efficient in high dimensions. Well suited for multi-modal problems. Efficient in high dimensions. Computational complexity exponential in number of parameters.
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Gridding or uniform sampling
Efficient in high dimensions
Well suited for multi-modal problems
Efficient in high dimensions
Computational complexity exponential in number of parameters
Number of local minima increases with object complexity
Not well suited for highly multi-modal problems
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.
Similarly to object localization considered in prior art, we estimate object’s position from manipulator data.
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.
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.
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.
Anna Petrovskaya, Oussama Khatib, Sebastian Thrun, Andrew Y. Ng
From Coarse to Fine Resolution
Scaling Series Algorithm
Sampling uniformly in (0.5m)3 x (360˚)3 with precision of 1mm and 1˚ requires 1015 samples. Instead we consider using “broad samples”, that represent regions of space. To do so, we blur measurement model in proportion to region radius.
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.
The approach can be summarized as follows:
e.g. importance sampling
or particle filter
Run Sampling Algorithm
Shrink Search Space to
High Likelihood Region
The mobile platform is teleoperated, but door handle probing and operation is completely autonomous.