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Experience-Based Surface-Discernment by a Quadruped Robot. by Lars Holmstrom, Drew Toland, and George Lendaris Portland State University, Portland, OR. Motivation for the Research. Enhance our Understanding of Intelligence
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Experience-BasedSurface-Discernment by aQuadruped Robot by Lars Holmstrom, Drew Toland, and George Lendaris Portland State University, Portland, OR
Motivation for the Research • Enhance our Understanding of Intelligence • Study aspects of how our minds work by implementing intelligent behavior in software and hardware • Application • Create intelligent tools and algorithms to solve complex problems in a more “human-like” way
Desirable Human-Like Abilities 1. Efficient transfer of knowledge from one problem domain to another
Desirable Human-Like Abilities 2. Rapid Context Discernment (System Identification)
Desirable Human-Like Abilities 3. The more knowledge one obtains, the more efficient one becomes at accessing and using that knowledge O(log n) search for binary trees
Experienced-Based Control • Goal: to build into machines the ability to use past experience when • performing system identification, and • coming up with a good controller for a given situation • To do so effectively and efficiently • To do so in a “human-like” fashion
Available Data • Vision • IR sensors • Accelerometers • Joint positions
AIBO Experience-Based Algorithm • Goal: AIBO to change gait based on surface type • Identify change in surface • Use only information available from joint actuators • Implement proper change to gait parameters • Recall gaits from previously-experienced surfaces • Desire to generalize to novel surfaces • Both tasks are to be based on past experiences with similar surfaces
How Do We Endow AIBO with Experience? • Base capability for walking behavior is provided by Sony (hardware) and Tekkotsu (software) • Train AIBO to develop “good” gaits (gait parameters) for a selected set of distinct surfaces
Genetic Algorithm Used to Develop/Learn Gaits • Optimized for a balance of speed and sway on 4 different surfaces 1. Hardboard 2. Thin foam 3. Thin carpet 4. Shag carpet • Each of these gaits performed significantly better than the default Tekkotsu gait (Chromosome)
Context Discernment • Our system now has Experience: Good GA gaits for a set of surfaces. • Now, how do we get AIBO to recognize and then adapt to changes in surface qualities?
Available Data • Vision • IR sensors • Accelerometers • Joint positions
Are There Measurable Differences Between the Kinesthetic Responses for Different Surface Types?
Complicating Attributes of Available Data for this Task • Low sampling rate (~31Hz) • Occasional dropped samples • Large variance • Process noise? • Measurement noise? • Time consuming to collect • Non-stationarities • Surface Irregularities • Physical Dynamics of the AIBO
Approach 1: Work in the Frequency Domain Smoothed Periodograms of Left Hip Joint, Thin-Foam Gait, on 4 Different Surfaces
Approach 2: Work in the Time Domain Linear Forward Prediction Model • For each of the 15 actuator signals, predict the current state of the actuator as a linear sum of the actuator’s past states. • Find the mean squared error (MSE) of the predictions across all of the actuators at each time step. • Using this procedure for a single gait/surface combination and set of actuator signals, we can generate a one-dimensional error signal
Fitting the Models • Fit one model on the data collected for each gait/surface combination • Solution of the Normal Equationsis performed to quickly find the unique and optimal model parameters for the given training data • Above properties motivated use of linear models (for computational ease)
Kinesthetic Experience(over all modeled gait/surface combinations) • The Kinesthetic Experience is the (#gaits • #surfaces) dimensional signal indicating each model’s MSE as it unfolds over time • In figure, error signal corres-ponding to the actual gait being used and surface being traversed is the minimum in each of these cases, indicating perfect classification
Implemented Discernment of Surface Transition • The algorithm can discern surface transitions in novel data within 2-4 seconds with 92% accuracy • Accuracy increased as time increases
The Same Approach Can Be Applied to Discerning Changes in Surface Incline Surface Transition Discernment Surface Discernment
Future Directions • Use a set of gait/surface experiences as the base for discerning novel surface types • The Kinesthetic Experience acts as a parametric “description” of the surface being experienced • Use the Kinesthetic Experience to generalize to new control policies to match new surfaces