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Software & Tutorials

Tutorials

Acoustic Sensing Starter Kit

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Acoustic Sensing is our latest exploration of adding tactile senses to the PneuFlex actuators. By embedding microphone into the air chamber, we can learn to recognize different contact states from sound alone. This tutorial explains how you, too, can get started with acoustic sensing. more to: Acoustic Sensing Starter Kit

PneuFlex Actuator Fabrication Tutorial

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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. more to: PneuFlex Actuator Fabrication Tutorial

The PnematicBox control system for soft hand control

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To control the inflation of the soft hand's pneumatic actuators we developed a custom controller board, which we call the "PneumaticBox". Here we provide an overview of the system, describe the hardware components, and link to our software stack. more to: The PnematicBox control system for soft hand control

Software

Online Interactive Perception

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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. more to: Online Interactive Perception

Learning State Representations with Robotic Priors

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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. more to: Learning State Representations with Robotic Priors

Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge

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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. more to: Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge

concarne: Learning with Side Information

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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. more to: concarne: Learning with Side Information

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