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.

为了在安全关键应用中验证机器人系统，需要进行多种场景的测试，包括不太可能发生的稀有边缘情况，需要通过在仿真中进行测试来补充真实世界的测试。我们提出了一种新颖的物理信息轨迹自编码器（PITA）架构，通过在自编码器的损失函数中融入物理动力学模型，从而得到既能重构输入轨迹又符合物理模型的平滑轨迹。