Unscented Autoencoder

The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. Interpreting its reconstruction process as a nonlinear transformation of samples from the latent posterior distribution, we apply the Unscented Transform (UT) -- a well-known distribution approximation used in the Unscented Kalman Filter (UKF) from the field of filtering. A finite set of statistics called sigma points, sampled deterministically, provides a more informative and lower-variance posterior representation than the ubiquitous noise-scaling of the reparameterization trick, while ensuring higher-quality reconstruction. We further boost the performance by replacing the Kullback-Leibler (KL) divergence with the Wasserstein distribution metric that allows for a sharper posterior. Inspired by the two components, we derive a novel, deterministic-sampling flavor of the VAE, the Unscented Autoencoder (UAE), trained purely with regularization-like terms on the per-sample posterior. We empirically show competitive performance in Fr\'echet Inception Distance (FID) scores over closely-related models, in addition to a lower training variance than the VAE

Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision

Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usually preferred for their simplicity, they relinquish powerful self-supervision schemes that can be constructed by chaining multiple time-steps. We address this issue by proposing a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments. In addition, the model 'imagines' the latent context and 'predicts the past' while combining multi-modal trajectories in a tree-like manner. We deliberately keep aspects such as interaction and environment modeling simplistic and nevertheless achieve competitive results on the INTERACTION dataset. Furthermore, we investigate the sparsely explored uncertainty estimation of deterministic predictors. We find positive correlations between the prediction error and two proposed metrics, which might pave way for determining prediction confidence.Comment: 8 pages, 6 figures, to be published in 34th IEEE Intelligent Vehicles Symposium (IV

Conditional Unscented Autoencoders for Trajectory Prediction

The \ac{CVAE} is one of the most widely-used models in trajectory prediction for \ac{AD}. It captures the interplay between a driving context and its ground-truth future into a probabilistic latent space and uses it to produce predictions. In this paper, we challenge key components of the CVAE. We leverage recent advances in the space of the VAE, the foundation of the CVAE, which show that a simple change in the sampling procedure can greatly benefit performance. We find that unscented sampling, which draws samples from any learned distribution in a deterministic manner, can naturally be better suited to trajectory prediction than potentially dangerous random sampling. We go further and offer additional improvements, including a more structured mixture latent space, as well as a novel, potentially more expressive way to do inference with CVAEs. We show wide applicability of our models by evaluating them on the INTERACTION prediction dataset, outperforming the state of the art, as well as at the task of image modeling on the CelebA dataset, outperforming the baseline vanilla CVAE. Code is available at https://github.com/boschresearch/cuae-prediction

Robotic Assistance in Medication Intake: A Complete Pipeline

During the last few decades, great research endeavors have been applied to healthcare robots, aiming to develop companions that extend the independent living of elderly people. To deploy such robots into the market, it is expected that certain applications should be addressed with repeatability and robustness. Such application is the assistance with medication-related activity, a common need for the majority of elderly people, referred from here on as medication adherence. This paper presents a novel and complete pipeline for assistance provision in monitoring and serving of medication, using a mobile manipulator embedded with action, perception and cognition skills. The challenges tackled in this work comprise, among others, that the robot locates the medication box placed in challenging spots by applying vision based strategies, thus enabling robust grasping. The grasping is performed with strategies that allow environmental contact, accommodated by the manipulator’s admittance controller which offers compliance behavior during interaction with the environment. Robot navigation is applied for the medication delivery, which, combined with active vision methods, enables the automatic selection of parking positions, allowing efficient interaction and monitoring of medication intake activity. The robot skills are orchestrated by a partially observable Markov decision process mechanism which is coupled with a task planner. This enables assistance scenario guidance and offers repeatability as well as gentle degradation of the system upon a failure, thus avoiding uncomfortable situations during human–robot interaction. Experiments have been conducted on the full pipeline, including robot’s deployment in 12 real house environments with real participants that led to very promising results with valuable findings for similar future applications