Learning Bayesian filters without precise ground truth

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 :




Origin Story of the OAK-D, with Brandon Gilles

Brandon Gilles, the founder of Luxonis and maker of the OAK-D, describes the journey and the flexibility of the OAK-D line of products
01 July 2022, by

The one-wheel Cubli

Researchers Matthias Hofer, Michael Muehlebach and Raffaello D’Andrea have developed the one-wheel Cubli, a three-dimensional pendulum system that can balance on its pivot using a single reaction wheel. How is it possible to stabilize the two tilt angles of the system with only a single reaction wheel?
30 June 2022, by and

At the forefront of building with biology

Raman is, as she puts it, “a mechanical engineer through and through.” Today, Ritu Raman leads the Raman Lab and is an Assistant Professor in the Department of Mechanical Engineering.
28 June 2022, by

Hot Robotics Symposium celebrates UK success

An internationally leading robotics initiative that enables academia and industry to find innovative solutions to real world challenges, celebrated its success with a Hot Robotics Symposium hosted across three UK regions last week.
25 June 2022, by

Researchers release open-source photorealistic simulator for autonomous driving

MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.
22 June 2022, by

In this episode, Audrow Nash speaks to Maria Telleria, who is a co-founder and the CTO of Canvas. Canvas makes a drywall finishing robot and is based in the Bay Area. In this interview, Maria talks ab...
21 June 2022, by and

©2021 - ROBOTS Association


©2021 - ROBOTS Association