PITA: Physics-Informed Trajectory Autoencoder
Johannes Fischer Kevin Rösch Martin Lauer Christoph Stiller
Validating robotic systems in safety-critical appli-cations requires testing
in many scenarios including rare edgecases that are unlikely to occur,
requiring to complement real-world testing with testing in simulation.
Generative models canbe used to augment real-world datasets with generated data
toproduce edge case scenarios by sampling in a learned latentspace.
Autoencoders can learn said latent representation for aspecific domain by
learning to reconstruct the input data froma lower-dimensional intermediate
representation. However, theresulting trajectories are not necessarily
physically plausible, butinstead typically contain noise that is not present in
the inputtrajectory. To resolve this issue, we propose the novel
Physics-Informed Trajectory Autoencoder (PITA) architecture, whichincorporates
a physical dynamics model into the loss functionof the autoencoder. This
results in smooth trajectories that notonly reconstruct the input trajectory
but also adhere to thephysical model. We evaluate PITA on a real-world dataset
ofvehicle trajectories and compare its performance to a normalautoencoder and a
state-of-the-art action-space autoencoder.