Robotics and Biology Laboratory


The Acoustic Sensing Starter Kit

With just a microphone and speaker, experience the power of acoustic sensing. From measuring contact on soft actuators to recognizing the type of shoe you're wearing, this innovative technology allows you to detect physical changes in objects by listening to the sounds they make. Join us in exploring the endless possibilities of acoustic sensing as we develop this cutting-edge technology. Get started now with our simple scripts and tutorials, and unleash your creativity!


RBO Hand 3 Compilation

The RBO Hand3 (RH3) Compilation is your guide to building the state-of-the-art RH3 developed at TU Berlin's Robotics and Biology Lab. From 3D printed pieces to silicone molded fingers and laser-cut components, we'll break down the process into manageable pieces to simplify the build. Watch our accompanying video tutorial on casting molds and assembling connector plates, and be on your way to creating your own RH3.


How to create a PneuFlex actuator

The PneuFlex actuator is a fiber-reinforced, pneumatic continuum actuator made almost entirely of soft materials. In this tutorial, we show you how to build them yourself.

The PneumaticBox control system for soft hand control

To control the inflation of the soft hand's pneumatic actuators we developed a custom controller board, which we call the "PneumaticBox". This system enables fast, realtime control of pneumatic systems. Here we provide an overview of the system, describe the hardware components, and link to our software stack.



Online Interactive Perception

Our Online Interactive Perception system extracts patterns of motion at different levels (point feature motion, rigid body motion, kinematic structure motion) and infers the kinematic structure and state of the interacted articulated objects. Optionally, it can reconstruct the shape of the moving parts and use it to improve tracking.


Learning State Representations with Robotic Priors

Instead of relying on human defined perception (mapping from observations to the current state) for a specific task, robots must be able to autonomously learn which patterns in their sensory input are important. We think that the can learn this by interacting with the world: performing actions, observing how the sensory input changes and which situations are rewarding. Here we provide the code related to our work on learning state representations with robotic priors.


Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge

In May 2015, our Team RBO won a prestigious international robotics challenge: The Amazon Picking Challenge. This challenge aims to solve one of the last problems in warehouse automation: identifying and grasping objects from a warehouse shelf. Here we provide the code and data for the object perception method of our winning entry.


concarne: Learning with Side Information

concarne is a lightweight python framework for learning with side information (aka privileged information). concarne implements a variety of different patterns that enable to apply side information.