Rollin' Justin, humanoid robot, Robotics and Mechatronics Center, Cologne, Germany

[Airicist]

Developer - Robotics and Mechatronics Center (https://pr.ai/showthread.php?1218)

Home page - rmc.dlr.de/justin (https://rmc.dlr.de/justin)

[Airicist]

https://youtu.be/R6pPwP3s7s4

Rollin' Justin Robot Catches Balls Tossed in its Direction

Uploaded on Apr 27, 2011


New controllers from DLR allow Rollin' Justin to catch balls tossed in its direction.

[Airicist]

https://youtu.be/3HaK9BQo40o

Rollin' Justin: Window Cleaning

Published on Apr 25, 2014


Object-Centered Hybrid Reasoning for Whole-Body Mobile Manipulation
This video features the humanoid robot Rollin' Justin of the German Aerospace Center (DLR), while wiping a window as a typical example for whole-body mobile manipulation. Hybrid reasoning is used to determine the optimal position of the robot regarding the task to be executed. Furthermore, the control parameters for the compliant behavior are specified during the planning phase. This way uncertainties and external disturbances can be compensated by the robot, while the task is accomplished.

[Airicist]

https://youtu.be/DnWqtisW81Q

Humanoid robot Justin dances Pulp Fiction song

Published on Jul 24, 2012


Humanoid robot Justin (developed by DLR, Germany) dances "Jackrabbit Slim's Twist Contest" from the movie Pulp Fiction

[Airicist]

https://youtu.be/jgIwgcz8iaM

Towards Intelligent Compliant Service Robots

Published on Jan 15, 2015


Towards Intelligent Compliant Service Robots
Daniel Leidner and Alexander Dietrich
Institute of Robotics and Mechatronics, German Aerospace Center (DLR)

[Airicist]

https://youtu.be/ZA2U9upkrjo

Planning and Execution of Daily Cleaning Tasks with the Humanoid Service Robot Rollin' Justin

Published on Mar 8, 2016


Universal robotic agents are envisaged to perform a wide range of manipulation tasks in everyday environments. A common action observed in many household chores is wiping, such as the absorption of spilled water with a sponge, skimming breadcrumbs off the dining table, or collecting shards of a broken mug using a broom. To cope with this versatility, the agents have to represent the tasks on a high level of abstraction. In this work, we propose to represent the medium in wiping tasks (\eg water, breadcrumbs, or shards) as generic particle distribution. This representation enables us to represent wiping tasks as the desired state change of the particles, which allows the agent to reason about the effects of wiping motions in a qualitative manner. Based on this, we develop three prototypical wiping actions for the generic tasks of absorbing, collecting and skimming. The Cartesian wiping motions are resolved to joint motions exploiting the free degree of freedom of the involved tool. Furthermore, the workspace of the robotic manipulators is used to reason about the reachability of wiping motions. We evaluate our methods in simulated scenarios, as well as in a real experiment with the robotic agent Rollin' Justin.

Daniel Leidner, Wissam Bejjani, Alin Albu-Sch?ffer, and Michael Beetz "Robotic Agents Representing, Reasoning, and Executing Wiping Tasks for Daily Household Chores", in Proc. of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Singapore, May 2016.

A draft version of the research paper is located at: https://elib.dlr.de/103272

[Airicist]

https://youtu.be/9ADtFJdLfQ8

An approach to combine balancing with hierarchical whole-body control for legged humanoid robots

Uploaded on May 20, 2016


“An Approach to Combine Balancing with Hierarchical Whole-Body Control for Legged Humanoid Robots,” by Bernd Henze, Alexander Dietrich, and Christian Ott from German Aerospace Center (DLR). Presented at ICRA 2016.

[Airicist]

https://youtu.be/623yRPx9Pkc

Agile Justin classifies materials by touch using deep learning

Published on Oct 18, 2016


In this paper we show that material classification purely based on the spatio-temporal signal of a flexible tactile skin mounted on the finger tip of the advanced humanoid robot Agile Justin can be robustly performed in a real world setting. We develop a convolutional deep learning network architecture which is directly fed with the raw 24000 dimensional sensor signal of the tactile skin. The network with its 16 million weights is trained from only 540 samples and reaches a classification accuracy of up to 97.3%.

S. Baishya and B. Bäuml. Robust material classification with a tactile skin using deep learning. In Proc. IEEE International Conference on Intelligent Robots and Systems, 2016.

[Airicist]

https://youtu.be/-jHQSVR8QT0

Rollin' Justin infers the effects of wiping motions using haptic perception

Published on Nov 14, 2016


Future service robots are expected to achieve high-quality task performance for everyday household chores. Some of the most frequent tasks in this domain are related to wiping of surfaces, such as vacuuming the floor, sweeping dust, or cleaning windows. However, the performance for these tasks is not directly observable as small dirt particles, dust, and residual water are hardly perceivable by means of computer vision. In this work we propose to utilize haptic perception paired with a qualitative effect representation to reason about the task performance of robotic wiping motions despite poor visual information. In particular, we relate the desired contact force to the measured end-effector force in order to simulate the effect of previously executed wiping motions. This way we are not just able to distinguish good from bad contact situations, but also replan recovery motions w.r.t. the effect-space to accomplish the commanded cleaning task subsequently. We evaluate our approach in a set of experiments with the robot Rollin' Justin.

Daniel Leidner and Michael Beetz "Inferring the Effects of Wiping Motions based on Haptic Perception", in Proc. of the International Conference on Humanoid Robots (ICHR), Mexico, November 2016.

A draft version of the research paper is located at: elib.dlr.de/107678 (https://elib.dlr.de/107678)

[Airicist]

https://youtu.be/jRMRkyD03N0

Alexander Gerst controls Justin humanoid robot from the ISS

Published on Aug 17, 2018


As part of ESA-led METERON (Multi-Purpose End-to-End Robotic Operations Network) project, Astronaut Alexander Gerst supervised and controlled the humanoid robot Justin to perform a series of tasks.

Credit:
Deutsche Zentrum für Luft- und Raumfahrt (DLR)
METERON project
Astronaut Alexander Gerst and humanoid robot Justin
17 August 2018

[Airicist]

https://youtu.be/TCXGNPgrDhw

METERON SUPVIS Justin experiment with Alexander Gerst

Streamed live Aug 17, 2018


ESA astronaut Alexander "Astro-Alex" Gerst commands the humanoid robot "Rollin' Justin" in the third METERON SUPVIS Justin experiment, live from the International Space Station (ISS). The the robot is located in a simulated Martian environment in the Robotics and Mechatronics Center of DLR in Oberpfaffenhofen, Germany.

In this ISS crew session, Alex will command Justin to perform some of the most complex space telerobotic tasks ever from orbit. Some of the new tasks include repair and hardware installation, with some of the most advanced robot dexterous capabilities to be shown in space telerobotics.

Our goal is to demonstrate the feasibility of using robots as coworkers on the planetary or lunar surface to eventually help build our first colony or habitat in space.

This project is led by DLR’s Robotics and Mechatronics Center, together with ESA’s Human Robot Interaction Lab, with partners including the German Space Operations Center, the European Astronaut Center, the Danish Aerospace Company, Airbus and NASA.

[Airicist]

https://youtu.be/LAu2avJoQq8

Providing assistance at a distance using the robotic avatar Rollin Justin

May 20, 2020


How can we provide assistance or care to people with infectious diseases or in quarantine?

This video shows the ways in which our robot Rollin' Justin can be operated to act as an avatar for friends, relatives or professional carers to assist people with restricted mobility, helping them to live safely and independently.

Justin can either be teleoperated using an intuitive tablet interface, or directly via a haptic device such as the DLR's HUG, which allows the user to see what Justin sees and feel the environments with which he interacts.