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Deep Learning Helicopter Dynamics Models
Ali Punjani Pieter Abbeel UC Berkeley EECS
Deep Learning Helicopter Dynamics Models Ali Punjani Pieter Abbeel - - PowerPoint PPT Presentation
Deep Learning Helicopter Dynamics Models Ali Punjani Pieter Abbeel - - PowerPoint PPT Presentation
Deep Learning Helicopter Dynamics Models Ali Punjani Pieter Abbeel UC Berkeley EECS Latent State: Airflow, Flexibility, Engine Dynamics etc. Similar trajectories have similar dynamics acceler acceleration ation stat state-contr e-control
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Similar trajectories have similar dynamics
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Similar trajectories have similar dynamics
acceler acceleration ation stat state-contr e-control tr
- l traject
ajector
- ry
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Need similarity and local dynamics
acceler acceleration ation stat state-contr e-control tr
- l traject
ajector
- ry
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Hierarchical Network Model
Input raw 0.5 second trajectory; Output acceleration
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Hierarchical Network Model
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Hierarchical Network Model
Jointly learn partitions of input space and local dynamics No labels or annotation
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Results on held-out test set Stanford Autonomous Helicopter Data
2 4 6 8 10
time (s)
−20 −10 10 20 30 40
Up-Down Acc. (ms−2)
circles
Observed Linear Acceleration Model ReLU Network Model
2 4 6 8 10
time (s)
−40 −30 −20 −10 10 20
Up-Down Acc. (ms−2)
flips-loops
Observed Linear Acceleration Model ReLU Network Model
2 4 6 8 10
time (s)
−40 −30 −20 −10 10 20 30 40
Up-Down Acc. (ms−2)
freestyle-aggressive
Observed Linear Acceleration Model ReLU Network Model
Ground Truth Baseline Model Our Model
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60% Improvement across all maneuvers
2 4 6 8 10 RMS up-down acceleration error (ms−2)
forward-sideways-flight
- rientation-sweeps
vertical-sweeps stop-and-go turn-demos1 inverted-vertical-sweeps
- rientation-sweeps-with-motion
dodging-demos4 circles flips-loops chaos turn-demos2 dodging-demos3 tictocs dodging-demos1 dodging-demos2 freestyle-gentle freestyle-aggressive turn-demos3
Up-Down Acceleration Error
Linear Acceleration Model Linear Lag Model Quadratic Lag Model ReLU Network Model
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Thanks!
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