Jason Yosinski

About Me

I'm currently a Ph.D. student in Computer Science at Cornell, where I study computers and brains. Some of my recent research has been on learning quadruped robot gaits with the Cornell Creative Machines Lab.

Papers more »

1. Jason Yosinski, Jeff Clune, Diana Hidalgo, Sarah Nguyen, Juan Cristobal Zagal, and Hod Lipson. “Evolving Robot Gaits in Hardware: the HyperNEAT Generative Encoding Vs. Parameter Optimization” Proceedings of the 20th European Conference on Artificial Life, Paris, France. pp 890-897. 8 August 2011.
   pdf | abstract▾ | bib▾

   Creating gaits for legged robots is an important task to enable robots to access rugged terrain, yet designing such gaits by hand is a challenging and time-consuming process. In this paper we investigate various algorithms for automating the creation of quadruped gaits. Because many robots do not have accurate simulators, we test gait-learning algorithms entirely on a physical robot. We compare the performance of two classes of gait-learning algorithms: locally searching parameterized motion models and evolving artificial neural networks with the HyperNEAT generative encoding. Specifically, we test six different parameterized learning strategies: uniform and Gaussian random hill climbing, policy gradient reinforcement learning, Nelder-Mead simplex, a random baseline, and a new method that builds a model of the fitness landscape with linear regression to guide further exploration. While all parameter search methods outperform a manually-designed gait, only the linear regression and Nelder-Mead simplex strategies outperform a random baseline strategy. Gaits evolved with HyperNEAT perform considerably better than all parameterized local search methods and produce gaits nearly 9 times faster than a hand-designed gait. The best HyperNEAT gaits exhibit complex motion patterns that contain multiple frequencies, yet are regular in that the leg movements are coordinated.

   @InProceedings{Yosinski2011EvolvedGaits,
     author =       {Jason Yosinski and Jeff Clune and Diana Hidalgo and Sarah Nguyen and Juan Cristobal Zagal and Hod Lipson},
     title =        {Evolving Robot Gaits in Hardware: the HyperNEAT Generative Encoding Vs. Parameter Optimization},
     booktitle =    {Proceedings of the 20th European Conference on Artificial Life},
     year =         {2011},
     month =        {August},
     numpages =     {8},
     location =     {Paris, France},
   }
   

2. Jason Yosinski and Cooper Bills. “MAV Stabilization using Machine Learning and Onboard Sensors” Technical Report CS6780, Cornell University (Posted to ArXiv 20 Feb 2012). 10 December 2010.
   pdf | abstract▾ | bib▾

   In many situations, Miniature Aerial Vehicles (MAVs) are limited to using only on-board sensors for navigation. This limits the data available to algorithms used for stabilization and localization, and current control methods are often insufficient to allow reliable hovering in place or trajectory following. In this research, we explore using machine learning to predict the drift (flight path errors) of an MAV while executing a desired flight path. This predicted drift will allow the MAV to adjust it’s flightpath to maintain a desired course.

   @techreport{yosinski2010mav,
     title={MAV Stabilization using Machine Learning and Onboard Sensors},
     author={Yosinski, J. and Bills, C.},
     year={2010},
     institution={Technical Report CS6780, Cornell University}
   }
   
   

3. Scott Lundberg, Randy Paffenroth, and Jason Yosinski. “Analysis of CBRN Sensor Fusion Methods” 2010 13th Conference on Information Fusion (FUSION). 26 July 2010.
   pdf | abstract▾ | bib▾

   Many well-defined metrics have been developed for data fusion problems in the target tracking domain. However, much less is known about the proper evaluation of Chemical, Biological, Radiological, and Nuclear (CBRN) data fusion methods. In this paper we begin to address the issue of evaluating the performance of CBRN fusion algorithms. As a demonstration of the applicability of modified target tracking metrics to CBRN problems we apply such techniques to an illustrative data fusion method. Of the metrics we have studied one stands out among the rest, namely covariance consistency, and we pay special attention to its proper application to CBRN scenarios. Covariance consistency is of particular importance since it allows us to evaluate how accurately an algorithm is reporting the uncertainty of its estimates, and we would contend that uncertainty measures are of paramount importance in CBRN scenarios.

   @inproceedings{lundberg2010analysis,
     title={Analysis of CBRN sensor fusion methods},
     author={Lundberg, S. and Paffenroth, R. and Yosinski, J.},
     booktitle={Information Fusion (FUSION), 2010 13th Conference on},
     pages={1--8},
     year={2010},
     month={july},
     keywords={CBRN sensor fusion methods;chemical-biological-radiological-nuclear data fusion;covariance consistency metric;target tracking domain;sensor fusion;target tracking;},
     organization={IEEE}
   }
   
   

4. Jason Yosinski and Randy Paffenroth. “Nonlinear Estimation for Arrays of Chemical Sensors” Signal and Data Processing of Small Targets (SPIE 2010). Orlando, Florida. Proc. SPIE Vol. 7698, 769809. 5 April 2010.
   pdf | abstract▾ | bib▾

   Reliable detection of hazardous materials is a fundamental requirement of any national security program. Such materials can take a wide range of forms including metals, radioisotopes, volatile organic compounds, and biological contaminants. In particular, detection of hazardous materials in highly challenging conditions - such as in cluttered ambient environments, where complex collections of analytes are present, and with sensors lacking specificity for the analytes of interest - is an important part of a robust security infrastructure. Sophisticated single sensor systems provide good specificity for a limited set of analytes but often have cumbersome hardware and environmental requirements. On the other hand, simple, broadly responsive sensors are easily fabricated and efficiently deployed, but such sensors individually have neither the specificity nor the selectivity to address analyte differentiation in challenging environments. However, arrays of broadly responsive sensors can provide much of the sensitivity and selectivity of sophisticated sensors but without the substantial hardware overhead. Unfortunately, arrays of simple sensors are not without their challenges - the selectivity of such arrays can only be realized if the data is first distilled using highly advanced signal processing algorithms. In this paper we will demonstrate how the use of powerful estimation algorithms, based on those commonly used within the target tracking community, can be extended to the chemical detection arena. Herein our focus is on algorithms that not only provide accurate estimates of the mixture of analytes in a sample, but also provide robust measures of ambiguity, such as covariances.

   @InProceedings{Yosinski2010ChemArrays,
     author =       {Jason Yosinski and Randy Paffenroth},
     editor =       {Oliver E. Drummond},
     title =        {Nonlinear Estimation for Arrays of Chemical Sensors.},
     booktitle =    {Proceedings of Signal and Data Processing of Small Targets (SPIE 2010)},
     year =         {2010},
     month =        {April},
     volume =       {7698},
     number =       {1},
     eid =          {769809},
     numpages =     {11},
     location =     {Orlando, Florida, USA},
   }
   

Personal

• A bit of science fiction I wrote for a class.
• Fall Break 2011 trip to Lake George with other grad students.
• Flood cleanup in Owego, NY.
• Five week Europe and San Francisco conference trip during the summer of 2011.
• Fireworks on the First of July, because Ithaca is weird.
• Backpacking trip in Ithaca.
• Cross-country skiing for the first time, in Ithaca.
• Trip to Vermont with my roommates.
• See some pictures from my summer 2010 backpacking trip through South America.
• Find me on Facebook, Twitter, and Github.