IPAB Workshop - 18/04/19

Todor Davchev

 

Title: Learning spatial representations and global dynamics for structured long term motion prediction

 

Description:

Context plays a significant role in the generation of motion for dynamic agents in interactive environments. This work proposes a modular method that utilises a model of the environment to aid trajectory prediction. We show that modelling both the spatial and dynamic aspects of a given scenario facilitates long-term prediction. Further, we observe that given an accurate static spatial representation, the proposed solution can generalise to unseen environments better than state-of-the-art alternatives. We highlight the model's prediction capability using a benchmark pedestrian tracking problem and a tabletop manipulation task where trajectories are generated conditioned on a plan learned from observations of another agent.

 

 

Martin Asenov

 

Title: Active Localization of Gas Leaks Using Fluid Simulation

 

Abstract: Sensors are routinely mounted on robots to acquire various forms of measurements in spatiotemporal fields. Locating features within these fields and reconstruction (mapping) of the dense fields can be challenging in resource-constrained situations, such as when trying to locate the source of a gas leak from a small number of measurements. In such cases, a model of the underlying complex dynamics can be exploited to discover informative paths within the field. We use a fluid simulator as a model to guide inference for the location of a gas leak. We perform localization via minimization of the discrepancy between observed measurements and gas concentrations predicted by the simulator. Our method is able to account for dynamically varying parameters of wind flow (e.g., direction and strength) and its effects on the observed distribution of gas. We develop algorithms for offline inference as well as for online path discovery via active sensing. We demonstrate the efficiency, accuracy, and versatility of our algorithm using experiments with a physical robot conducted in outdoor environments. We deploy an unmanned air vehicle mounted with a CO2 sensor to automatically seek out a gas cylinder emitting CO2 via a nozzle. We evaluate the accuracy of our algorithm by measuring the error in the inferred location of the nozzle, based on which we show that our proposed approach is competitive with respect to state-of-the-art baselines.

Apr 18 2019 -

IPAB Workshop - 18/04/19

Martin Asenov, Todor Davchev

IF, 4.31/33