Hidden Markov Model Approach to Skill Learning and Its ...

Hidden Markov Model Approach to Skill Learning and Its ...

Skill Learning in Telerobotics using Hidden Markov Model by Gary Holness Skill Learning

Human performance stochastic Repeated trials of same task different Something about the task

Uncovering nature of data Most likely performance rejecting noise Stochastic methods perfect Why HMM? Double stochastic process - observable process (motion data) - hidden process (mental state/intent)

Parametric model with incremental update Observations as symbols Unifying framework for perception and action Likely human performance from measured activity Software architecture

Pre-processing to extract observation symbols Algorithm on real-data or simulation SM configuration 2 7 DOF

6 DOF free flying hand controller provide control input HMM experiment Orbit replacement unit (ORU) Nut driver in gripper Model action under teleoperator control as HMM

Position/trajectory in Cartesian space Position/trajectory in Joint space Velocity/trajectory in Cartesian space HMM Experiment contd Observable symbols: trajectory States: subtasks Special case on states: time index

increases left-to-right (Bakis model) Fewer parameters than ergodic HMM 100 trajectories recorded and scored Position trajectory in Cartesian space Forward algorithm used for scoring: P(O|)

Trajectory 60,90 better than average Score increase w.r.t iteration model improvement Position trajectory in joint space Iteration 77 best score Velocity in Cartesian, iteration 49 is best

What was good? Use mathematical framework for which many statistical tools already exist Integrating framework Software engineering Rigorous: make sound statements about experiment (not just it works therefore its proven)

Clearly laid out design What was bad? Poor initialization: Baum-Welch can converge to local maxima (not problem of experiment) Simplification in left-right HMM (understandable why they did it)

Independence assumption among r.v. in R-dimensional observation vector (joints non-independent) Why do I care? Learning in HMM for ergodic case Choose right features as observation symbols

Make use of 80-years of statistical tools Beautifully engineered research artifacts Framework for skills transfer and re-use Transition among HMMs still an HMM: lends itself to hierarchical descriptions Conclusion

Representation, representation, representation

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