Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks. While in the past, robot programs were often written statically and tuned manually, the current, faster transition times call for robust, modular and interpretable solutions that also allow a robotic system to learn how to perform a task. We propose the method Behavior-based Bayesian Optimization and Planning (BeBOP) that combines two approaches for generating behavior trees: we build the structure using a reactive planner and learn specific parameters with Bayesian optimization. The method is evaluated on a set of robotic manipulation benchmarks and is shown to outperform state-of-the-art reinforcement learning algorithms by being up to 46 times faster while simultaneously being less dependent on reward shaping. We also propose a modification to the uncertainty estimate for the random forest surrogate models that drastically improves the results.

In 2014, we wrote about some failsafe software from ETH Zurich that allowed a quadrotor to remain fully controllable even with one busted motor. The unbalanced torque generated by three motors means that a quadrotor can't help but spin, but with a bit of cleverness, software can compensate for the spin and keep the quadrotor stable and even allow it to obey control inputs, allowing it to land more or less safely. This is a valuable capability, but there are a few things that it doesn't address. For example, what if your quadrotor loses a rotor over an unsafe area? What if something happens to it when it's already traveling at a high speed?