1 Predictive Exploration for Autonomous Science David R. Thompson AAAI Doctoral Consortium 2007 Robotics Institute Carnegie Mellon University NASA ASTEP NNG0-4GB66G

2 Agenda ● Overview of predictive exploration ● Component technologies ● Future work

3 Image Courtesy NASA / JPL-Caltech / Cornell

4 Courtesy USGS Courtesy NASA The Remote Exploration Problem ● Mobile autonomous explorer ● Severe constraints on bandwidth, communications, time and energy ● Collect and return data to facilitate best reconstruction of explored environment

5 spatial inference cross-sensor inference ? ? Model and exploit the environment's structure Approach

6 P(  ) P(  |X) H(Θ) = -∫P(Θ) log P(Θ) dΘ Treat exploration as experimental design Learn a model of the environment on the fly Posterior entropy prescribes optimal data collection and return Approach

7 collectio n returned data collected data environment downlink min H( environment | returned data ) Objective Function

8 min X,X' H(Θ|X') collectio n,Θ,Θ downlink XX' environment collected datareturned data Objective Function

9 min X,X' H(Θ|X') ∝ -H(, X) + H(X|X') Data Collection Maximum Entropy Sampling [Sebastiani and Wynn, 2000] Data Return Combinatorial Optimization collectio n,Θ,Θ downlink XX' environment collected datareturned data Objective Function

10 Agenda ● Overview of predictive exploration ● Component technologies ● Future work

11 feature detection, classification data model (map) action selection surface images, spectra navigation, data collection selective downlink orbital data Component Technologies

12 Rock Detection slow and accurate quick and dirty [ Castaño et al 2004, Thompson et al. 2005, Thompson and Castaño 2007 ] feature detecti on data model action selecti on

13 Texture features (textons) feature detecti on data model action selecti on

14 multispectral pixel intensities Orbital Imagery feature detecti on data model action selecti on

15 feature detection, classification data model (map) action selection surface images, spectra selective downlink orbital data navigation, data collection Component Technologies

16 Gaussian Processes feature detecti on data model action selecti on A Bayesian nonparametric model for a distribution over functions Posterior prediction over any sample points is jointly Gaussian

17 Covariance Functions feature detecti on data model action selecti on Determine a prior over the function space Stationary covariance function Nonstationary covariance

18 Information-optimal subsampling of rover transect imagery feature detecti on data model action selecti on feature detecti on data model action selecti on

19 Informative Path Planning feature detecti on data model action selecti on feature detecti on data model action selecti on

20 Agenda ● Overview of predictive exploration ● Component technologies ● Future work

21 Spectral mapping Regress location, orbital, visible data onto VisNIR spectrometer measurements of detected rocks Visual servoing: 75% accuracy to 6m

22 ~50m ~1000m Hypothesis Compared with “uniform coverage” methods, Predictive exploration yields superior reconstruction fidelity Experiments ● Simulation in progress ● Kilometer-scale field traverses in the American Southwest (Oct 07)

23 uniform coverage predictive exploration

24 Summary ● Remote resource-constrained exploration as classical experimental design ● Information-theoretic notion of science value – an appropriate model is the key!

25 Questions?

26 Information- Optimal Robot Mapping Generative spatial models Frontier- driven exploration Bayesian experimental design Predictive Exploration Predictive