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.

Publications

Proceedings

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. 8-12 August 2011. pp 890-897.
   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 Randy Paffenroth. "Nonlinear Estimation for Arrays of Chemical Sensors." Signal and Data Processing of Small Targets (SPIE 2010). Orlando, Florida. 5-9 April 2010. Proc. SPIE Vol. 7698, 769809.
   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},
   }
   

3. Scott Lundberg, Randy Paffenroth, and Jason Yosinski. "Algorithms for Distributed Chemical Sensor Fusion," Signal and Data Processing of Small Targets (SPIE 2010). Orlando, Florida. 5-9 April 2010. Proc. SPIE Vol. 7698, 769806.
   pdf | abstract▾ | bib▾

   The fusion of Chemical, Biological, Radiological, and Nuclear (CBRN) sensor readings from both point and stand-off sensors requires a common space in which to perform estimation. In this paper we suggest a common representational space that allows us to properly assimilate measurements from a variety of different sources while still maintaining the ability to correctly model the structure of CBRN clouds. We design this space with sparse measurement data in mind in such a way that we can estimate not only the location of the cloud but also our uncertainty in that estimate. We contend that a treatment of the uncertainty of an estimate is essential in order to derive actionable information from any sensor system; especially for systems designed to operate with minimal sensor data. A companion paper further extends and evaluates the uncertainty management introduced here for assimilating sensor measurements into a common representational space.

   @InProceedings{Yosinski2010DistributedChem,
     author =       {Scott Lundberg and Randy Paffenroth and Jason Yosinski},
     editor =       {Oliver E. Drummond},
     title =        {Algorithms for Distributed Chemical Sensor Fusion},
     booktitle =    {Proceedings of Signal and Data Processing of Small Targets (SPIE 2010)},
     year =         {2010},
     month =        {April},
     volume =       {7698},
     number =       {1},
     eid =          {769806},
     numpages =     {12},
     location =     {Orlando, Florida, USA},
   }
   

4. Jason Yosinski, Nick Coult, and Randy Paffenroth. "Network-centric Angle Only Tracking," Signal and Data Processing of Small Targets (SPIE 2009). San Diego, California. 2-6 August 2009. Proc. SPIE Vol. 7445, 74450O.
   pdf | abstract▾ | bib▾

   The coordinated use of multiple distributed sensors by network communication has the potential to substantially improve track state estimates even in the presence of enemy countermeasures. In the modern electronic warfare environment, a network-centric tracking system must function in a variety of jamming scenarios. In some scenarios hostile electronic countermeasures (ECM) will endeavor to deny range and range rate information, leaving friendly sensors to depend on passive angle information for tracking. In these cases the detrimental effects of ECM can be at least partially ameliorated through the use of multiple networked sensors, due to the inability of the ECM to deny angle measurements and the geometric diversity provided by having sensors in distributed locations. Herein we demonstrate algorithms for initiating and maintaining tracks in such hostile operating environments with a focus on maximum likelihood estimators and provide Cramer-Rao bounds on the performance one can expect to achieve.

   @InProceedings{Yosinski2009AngleOnly,
     author =       {Jason Yosinski and Nick Coult and Randy Paffenroth},
     editor =       {Oliver E. Drummond and Richard D. Teichgraeber},
     title =        {Network-centric Angle Only Tracking},
     booktitle =    {Proceedings of Signal and Data Processing of Small Targets (SPIE 2009)},
     year =         {2009},
     month =        {August},
     volume =       {7445},
     number =       {1},
     eid =          {74450O},
     numpages =     {11},
     location =     {San Diego, California, USA},
   }
   

5. Jason Yosinski and Randy Paffenroth. "A Distributed Database View of Network Tracking Systems" Signal and Data Processing of Small Targets (SPIE 2008). Orlando, Florida. 16-20 March 2008. Proc. SPIE Vol. 6969, 696915.
   pdf | abstract▾ | bib▾

   In distributed tracking systems, multiple non-collocated trackers cooperate to fuse local sensor data into a global track picture. Generating this global track picture at a central location is fairly straightforward, but the single point of failure and excessive bandwidth requirements introduced by centralized processing motivate the development of decentralized methods. In many decentralized tracking systems, trackers communicate with their peers via a lossy, bandwidth-limited network in which dropped, delayed, and out of order packets are typical.

   Oftentimes the decentralized tracking problem is viewed as a local tracking problem with a networking twist; we believe this view can underestimate the network complexities to be overcome. Indeed, a subsequent 'oversight' layer is often introduced to detect and handle track inconsistencies arising from a lack of robustness to network conditions.

   We instead pose the decentralized tracking problem as a distributed database problem, enabling us to draw inspiration from the vast extant literature on distributed databases. Using the two-phase commit algorithm, a well known technique for resolving transactions across a lossy network, we describe several ways in which one may build a distributed multiple hypothesis tracking system from the ground up to be robust to typical network intricacies. We pay particular attention to the dissimilar challenges presented by network track initiation vs. maintenance and suggest a hybrid system that balances speed and robustness by utilizing two-phase commit for only track initiation transactions. Finally, we present simulation results contrasting the performance of such a system with that of more traditional decentralized tracking implementations.

   @InProceedings{Yosinski2008DistributedTracking,
     author =       {Jason Yosinski and Randy Paffenroth},
     editor =       {Oliver E. Drummond},
     title =        {"A Distributed Database View of Network Tracking Systems"},
     booktitle =    {Proceedings of Signal and Data Processing of Small Targets (SPIE 2008)},
     year =         {2008},
     month =        {March},
     volume =       {6969},
     number =       {1},
     eid =          {696915},
     numpages =     {12},
     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.