Agile Justin

The humanoid robot Agile Justin is a platform for research in learning artificial intelligence (AI) for dextrous manipulation. Areas of application for the system are, in particular, household work and assisting astronauts in space.

The robot is continuously upgraded, its earliest predecessor was presented to the public in 2008.

Technical data

Size	Adult human (1.91 m)
Weight	45kg (upper body) + 150 kg (platform)
Degrees of freedom	53 (platform: 8, arms: 2 × 7, hands: 2 × 12 + 1, torso: 3, neck: 2,)
Nominal load capacity	15 kg
Energy supply	Battery with operating time of > 60 min
Speed	2 m/s or 7.2 km/h
Working Space	From the floor to a height of 2.7 m
Special features	• Torque sensors in all joints • Whole-body control: 1 kHz rate (over all DOF) • Head with 2 stereo cameras & RGB-D cameras • Tactile skin with resolution: 1-5cm on body & 2mm on hands • Computing: onboard: 4x Intel Xeon Quadcore
External	GPGPU server & Cloud access

System description

Agile Justin is one of the worldwide most advanced humanoid robots for mobile manipulation. It is equipped with sensorial and motor skills coming close to the human. DLR’s Autonomous Learning Lab uses Agile Justin as an ideal platform for research on modern learning artificial intelligence (AI) architectures, esp. generative AI and deep reinforcement learning (RL). The lab investigates learning as the core principle in perception, modeling and action in autonomous systems, which operate in complex and perpetually changing environments. Its most important upgrades compared to Rollin’Justin are the high resoultion tactile skin on the fingertips and the whole body, the wirelessly coupled large computing resources with a CPU und GPU cluster and the realtime distributed communication framework aRDx , developed specifically for the development of learning AI applications.

Awards (selection)

2016 ICRA Best Cognitive Paper Award Finalist
2016 ICRA Best Student Paper Award Finalist https://www.youtube.com/watch?v=623yRPx9Pkc
2014 ICRA Best Video Award https://www.youtube.com/watch?v=Fl2N6yZrk1o
2012 ICRA Best Vision Paper Award Finalist
2011 ICRA Best Video Award Finalist https://www.youtube.com/watch?v=R6pPwP3s7s4
2011 IROS Best Student Paper Award Finalist

Publications

A selection of recent publications:

J. Pitz, L. Röstel, L. Sievers, and B. Bäuml. Dextrous tactile in-hand manipulation using a modular renforcement learning architecture. In Proc. IEEE International Conference on Robotics and Automation, 2023.
Lennart Röstel, Leon Sievers, Johannes Pitz, and Berthold Bäuml. Learning a state estimator for tactile in-hand manipulation. In Proc. Int. Conf. Intelligent Robots and Systems, 2022
Leon Sievers, Johannes Pitz, and Berthold Bäuml. Learning purely tactile in-hand manipulation with a torque-controlled hand. In Proc. IEEE International Conference on Robotics and Automation, 2022.
Johannes Tenhumberg and Berthold Bäuml. Massively speeding up optimization-based motion planning through deep learning. In Proc. Int. Conf. Intelligent Robots and Systems 2022.
Dominik Winkelbauer and Berthold Bäuml. A two-stage learning architecture that generates high-quality grasps for a multi-fingered hand. In Proc. Int. Conf. Intelligent Robots and Systems, 2022.