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This page lists software, web services and tutorials that are developed and maintained as part of our research at RBO Lab, and which are publicly available for private use and research.

Code

Online Interactive Perception

Our code of our perceptual system for Online Interactive Perception of articulated objects. It includes our methods presented in at IROS14 (PDF, 1,8 MB) and ICRA16 (PDF, 2,3 MB). The 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. It uses an RGB-D stream of an interaction as input. Optionally, it can reconstruct the shape of the moving parts and use it to improve tracking. More info at omip wiki.

Repository: https://github.com/tu-rbo/omip and https://github.com/tu-rbo/omip_msgs

Accompanies papers: Online Interactive Perception of Articulated Objects with Multi-Level Recursive Estimation Based on Task-Specific Priors (PDF, 1,8 MB) and An Integrated Approach to Visual Perception of Articulated Objects (PDF, 2,3 MB)

Maintainers: Roberto Martín-Martín

Project: Interactive Perception

Learning State Representations with Robotic Priors

This simple python script contains the essence of our work on learning state representations with robotic priors and complements the paper to provide sufficient detail for reproducing our results and for reusing the method in other research while minimizing code overhead.

Repository: github.com/tu-rbo/learning-state-representations-with-robotic-priors

Accompanies paper: Learning State Representations with Robotic Priors

Maintainers: Rico Jonschkowski

Project: Representation Learning

Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge

This repository includes the code and data for the object perception method of our winning entry to the 2015 Amazon Picking Challenge. The object segmentation method and the analysis from the accompaning paper are implemented in python. The data can be used to develop and benchmark new perception methods in this setting.

Repository: gitlab.tubit.tu-berlin.de/rbo-lab/rbo-apc-object-segmentation

Accompanies paper: Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge

Maintainers: Rico Jonschkowski

Project: Amazon Picking Challenge 2015

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. As it builds upon Theano and lasagne, you can use neural network structures that you have developed yourself and easily combine them with the side information learning task.

Repository: https://github.com/tu-rbo/concarne

Accompanies paper: Patterns for Learning with Side Information

Maintainers: Sebastian Höfer, Rico Jonschkowski

Research project: Representation Learning

 

 

Webservices

RBO Aleph: A Web Service for Automated Protein Structure Prediction

RBO Aleph provides a fully automatic protein structure prediction pipeline. The focus of our system is ab initio structure prediction. 

RBO Aleph has been evaluated to be one of the leading ab initio structure prediction methods in CASP11.

A web interface of to RBO Aleph is available here:

http://compbio.robotics.tu-berlin.de/rbo_aleph/

Contact: Mahmoud Mabrouk

EPC-map: Combining Evolutionary and Physicochemical Information for Contact Prediction

Our approach integrates decoy-based contact prediction with machine learning and evolutionary information from multiple-sequence alignments, called EPC-map (using Evolutionary and Physicochemical information to predict Contact maps).

This method ranked 2nd for long+medium range contacts and 5th for long-range contacts in CASP11.

A web service implementing this method is available here: 

http://compbio.robotics.tu-berlin.de/epc-map/

Contact: Kolja Stahl

 

 

Tutorials

Tutorial on building PneuFlex actuators

PneuFlex actuators are a version of so called soft pneumatic continuum actuators. Their production process is relatively simple, and can accomodate complex actuator shapes. This tutorial explains how to build them.

Contact: Raphael Deimel, Vincent Wall

Pneumaticbox Control System

The Pneumaticbox is a specific hardware combined with an embedded software system to provide a complete control solution for PneuFlex actuators:

  • The Hardware consists of valves, pressure sensors, common I/O interfaces (USB, SPI, I2C), and an embedded computer.
  • The python-pneumaticbox is the Python client library. It can be used for easy integration into existing systems or direct scripting of experiments.

 

Related publications:


Contact: Raphael Deimel, Vincent Wall

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