Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e.g., selecting different planning algorithms). Once an algorithm has been selected for a component, its associated configuration options must be set to the appropriate values. Configuration options across the system stack interact non-trivially. Finding optimal configurations for highly configurable robots to achieve desired performance poses a significant challenge due to the interactions between configuration options across software and hardware that result in an exponentially large and complex configuration space. These challenges are further compounded by the need for transferability between different environments and robotic platforms. Data efficient optimization algorithms (e.g., Bayesian optimization) have been increasingly employed to automate the tuning of configurable parameters in cyber-physical systems. However, such optimization algorithms converge at later stages, often after exhausting the allocated budget (e.g., optimization steps, allotted time) and lacking transferability. This paper proposes CURE -- a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance. CURE abstracts the causal relationships between various configuration options and robot performance objectives by learning a causal model in the source (a low-cost environment such as the Gazebo simulator) and applying the learned knowledge to perform optimization in the target (e.g., Turtlebot 3 physical robot). We demonstrate the effectiveness and transferability of CURE by conducting experiments that involve varying degrees of deployment changes in both physical robots and simulation.

Robotic systems have subsystems with a combinatorially large configuration space and hundreds or thousands of possible software and hardware configuration options interacting non-trivially. The configurable parameters are set to target specific objectives, but they can cause functional faults when incorrectly configured. Finding the root cause of such faults is challenging due to the exponentially large configuration space and the dependencies between the robot's configuration settings and performance. This paper proposes CaRE -- a method for diagnosing the root cause of functional faults through the lens of causality. CaRE abstracts the causal relationships between various configuration options and the robot's performance objectives by learning a causal structure and estimating the causal effects of options on robot performance indicators. We demonstrate CaRE's efficacy by finding the root cause of the observed functional faults and validating the diagnosed root cause by conducting experiments in both physical robots (Husky and Turtlebot 3) and in simulation (Gazebo). Furthermore, we demonstrate that the causal models learned from robots in simulation (e.g., Husky in Gazebo) are transferable to physical robots across different platforms (e.g., Husky and Turtlebot 3).

Any time of year is the perfect time to buy a robot for yourself or someone who needs more robots in their life, but this particular time of year is even perfecter than most: The holidays are approaching, all kinds of things are on sale, and nobody will ask questions if a whole bunch of new robots suddenly show up in your house. To help you decide which robots to buy for yourself and which to buy for yourself and for other people, we've put together a brand new edition of our annual Robots Gift Guide. It's stuffed with giftable robots ranging from affordable to ridiculous, and we promise that if you don't find something you like, we'll feel bad about it and be sad. Also, don't forget that we've got robot gift guides going back like five years (here: 2016, 2015, 2014, 2013, 2012), and since we try to mix them up every year, they're great places for even more ideas for robots that are probably way cheaper now than when we first posted about them. And remember: While we provide prices and links to places where you can buy these items, we're not endorsing any in particular, and a little bit of searching may result in better deals (all prices are in U.S. dollars).

Today, Clearpath Robotics (in partnership with Intel and iRobot) is announcing the newest member of the TurtleBot family: the TurtleBot Euclid. The TB Euclid, which should probably not be abbreviated as TBe, features an iRobot Create 2 mobile base along with a shiny new Intel Euclid sensing and computing module. It's designed to be both easier to use and cheaper than the original Turtlebot 2, and makes us more certain than ever that yes, TurtleBots are taking over the world. It's no secret that we love TurtleBots. We love big TurtleBots, and we love little Turtlebots, but we especially love TurtleBots that are brand new, and that makes TurtleBot Euclid our absolute favorite right now.

South Korean robotics company Robotis and the Open Source Robotics Foundation (OSRF) announced the TurtleBot 3 at ROSCon last year in Seoul. We got to see a wide variety of prototypes, but Robotis was still in the middle of figuring out exactly what TurtleBot 3 was going to look like and what hardware it would include. The company told us at the time that they wanted the robot to be as open, modular, and customizable as possible, and we've been waiting excitedly to see what they came up with.

South Korean robotics company Robotis and the Open Source Robotics Foundation (OSRF) announced the TurtleBot 3 at ROSCon last year in Seoul. We got to see a wide variety of prototypes, but Robotis was still in the middle of figuring out exactly what TurtleBot 3 was going to look like and what hardware it would include. The company told us at the time that they wanted the robot to be as open, modular, and customizable as possible, and we've been waiting excitedly to see what they came up with. Today, Robotis is finally ready to share the brand-new TurtleBot 3 with the world. And, surprise, there are actually two TurtleBot 3 models: Burger and Waffle, so named because that's kind of what each of them looks like, if you're willing to stretch your imagination a bit: A few weeks ago, Robotis shipped us test units of the two models, and after putting them together and playing a bit with them, we've got an in-depth review for you along with all the info about price and availability.

Thousands of TurtleBots are out in the world right now, providing a (mostly) straightforward and (mostly) affordable way to get started with ROS. They're (mostly) portable and (mostly) extendable, allowing you (with a limited amount of inconvenience) to modify the robot to keep up with your needs. TurtleBot 2 is a great platform (I certainly love mine), but its size and cost usually restrict it to people who already have some ROS experience, and know that a TurtleBot is something worth investing in. For people who want to get started with ROS but aren't prepared to make as much of an investment, there just aren't a lot of options with the same kind of community and support that you get with TurtleBot. At ROSCon this past weekend, the Open Source Robotics Foundation (OSRF) and South Korean robot maker ROBOTIS are tackling these problems by announcing a shiny new version of TurtleBot: TurtleBot 3. TB3 is small enough to fit into a backpack, and with a single-board computer instead of a netbook and just two Dynamixel motors driving a pair of wheels, it's both simpler than previous TurtleBots and significantly cheaper.