Robohub.org
 

Learning Bayesian filters without precise ground truth

by
21 January 2011



share this:

As seen in a previous post, Bayes filters such as Kalman filters can be used to estimate the state of a robot. Usually, this requires having a model of how the robot’s sensor measurements relate to the state you want to observe and a model to predict how the robot’s control inputs impact its state. When little is know about these models, machine learning techniques can help find models automatically.

In the scenario imagined by Ko et al. the goal is to infer a car’s position (state) on a race track based on remote control commands and measurements from an Inertial Measurement Unit (IMU, see red box in picture below) that provides turn rates in roll, pitch, and yaw and accelerations in 3 dimensions. The car is only controlled in speed since a “rail” in the track makes it turn. As a start, Ko et al. collect data by driving the car around the track while recording its remote control commands, IMU measurements, and position (ground truth) estimated using an overhead camera. The data is then used to train probabilistic models (Gaussian Process models) that are finally integrated into a Bayes filter.

However, the need for ground truth requires extra effort and additional hardware such as the overhead camera. To overcome this problem, Ko et al. extend their method to deal with situations where no, little, or noisy ground truth is used to train the models.

Results show the successful tracking of the car with better performance than state-of-the-art approaches, even when no ground truth is given. The authors also show how the developed method can be used to allow the car or a robot arm to replay trajectories based on expert demonstrations.




Sabine Hauert is President of Robohub and Associate Professor at the Bristol Robotics Laboratory
Sabine Hauert is President of Robohub and Associate Professor at the Bristol Robotics Laboratory





Related posts :



Open Robotics Launches the Open Source Robotics Alliance

The Open Source Robotics Foundation (OSRF) is pleased to announce the creation of the Open Source Robotics Alliance (OSRA), a new initiative to strengthen the governance of our open-source robotics so...

Robot Talk Episode 77 – Patricia Shaw

In the latest episode of the Robot Talk podcast, Claire chatted to Patricia Shaw from Aberystwyth University all about home assistance robots, and robot learning and development.
18 March 2024, by

Robot Talk Episode 64 – Rav Chunilal

In the latest episode of the Robot Talk podcast, Claire chatted to Rav Chunilal from Sellafield all about robotics and AI for nuclear decommissioning.
31 December 2023, by

AI holidays 2023

Thanks to those that sent and suggested AI and robotics-themed holiday videos, images, and stories. Here’s a sample to get you into the spirit this season....
31 December 2023, by and

Faced with dwindling bee colonies, scientists are arming queens with robots and smart hives

By Farshad Arvin, Martin Stefanec, and Tomas Krajnik Be it the news or the dwindling number of creatures hitting your windscreens, it will not have evaded you that the insect world in bad shape. ...
31 December 2023, by

Robot Talk Episode 63 – Ayse Kucukyilmaz

In the latest episode of the Robot Talk podcast, Claire chatted to Ayse Kucukyilmaz from the University of Nottingham about collaboration, conflict and failure in human-robot interactions.
31 December 2023, by





Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association