ANYmal, quadrupedal robot, Robotic Systems Lab, Institute of Robotics and Intelligent Systems, Zurich, Switzerland

[Administrator]

Robotic hike with ANYmal

Oct 21, 2020

Four legged robot ANYmal goes on a hike through the mountains in Tenna, Switzerland. It maneuvers two kilometers on a rough, rocky and uneven hiking path overcoming 82 meters of elevation gain.

 

[Administrator]

ANYmal Planet — Fresh waters

Nov 24, 2020

Water and robots typically don't mix... Lucky, that's exactly the case for ANYmal. Observe the autonomous species in its natural habitat—which just might be anywhere!

 

[Administrator]

ANYbotics introduces end-to-end robotic inspection solution

Apr 21, 2021

ANYbotics, the Swiss robotics company, is launching a new end-to-end robotic inspection solution for operators of energy and industrial processing plants. While demand for lower downtime and better safety increases on production sites, technologies have not yet caught up with the complex structures of industrial facilities. ANYbotics’ new fully autonomous four-legged robot ANYmal and it’s inspection analytics software provide a scalable solution to automate routine condition monitoring of equipment and infrastructure. The company has started onboarding early customers to bring robotic inspection into their facilities and is rolling out installations later this year.

 

[Administrator]

ANYbotics partners with Vale to prioritize people in the mining industry

Aug 17, 2021

In a partnership with ANYbotics, Vale highlights its commitment to becoming one of the safest

 

[Administrator]

Autonomous subterranean exploration with the ANYmal C Robot inside the Hagerbach underground mine

Sep 10, 2021

 

[Administrator]

ANYmal at DARPA SubT final run

Sep 27, 2021

This is a collection of all video snippets of the final run of the DARPA subT challenge broadcasted by DARPA featuring the ANYmal robots of team CERBERUS.

 

[Administrator]

Learning to walk in minutes using massively parallel deep RL

Oct 5, 2021

We present a training set-up that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU. The parallel approach allows training policies for flat terrain in under 4 minutes, and in 20 minutes for uneven terrain.

Paper accepted to CoRL 2021.
The corresponding code will be released soon.
Paper: https://arxiv.org/abs/2109.11978

 

[Administrator]

Recovery controller for a quadrupedal robot using deep reinforcement learning

Nov 18, 2021

Master's thesis by Joonho Lee (https://scholar.google.com/citations?...)

Paper link: https://arxiv.org/abs/1901.07517

 

[Administrator]

Meet ANYmal, one of the robots poised to go mainstream in 2022

Dec 27, 2021

It’s meant to save us from dangerous jobs in sewers and industrial plants, but on Zürich’s streets, the public finds this robot intriguing. As part of our Young Bright Minds series, we talk to engineers from Zürich-based robotics start-up ANYbotics to find out what inspired them to create this robot and others, and where they think robotics will go next.

 

[Administrator]

ANYmal X — the world’s first ex-proof legged robot

Mar 22, 2022

ANYbotics, the Swiss robotics company, introduces ANYmal X, the world's first Ex-proof legged robot. ANYmal X now makes it possible for the Oil & Gas and Chemical industries to automate routine inspections, thereby increasing safety and operational effectiveness. ANYmal X extends the leading mobility, autonomy, and inspection intelligence of ANYbotics' robotic inspection solution, and is specifically designed and certified for safe usage in hazardous and potentially explosive environments — a game changer for the industry.

ANYmal X - A remarkable engineering achievement

Mar 22, 2022

ANYmal X is the world's first robot to provide the advanced mobility of four legs in the potentially hazardous and explosive facilities in the Oil & Gas and Chemicals industries. ANYmal X extends the leading mobility, autonomy, and inspection intelligence of ANYbotics' robotic inspection solution, and is specifically designed and certified for safe usage in hazardous and potentially explosive environments — a game changer for the industry. The challenge of packing all inspection capabilities and performance into an Ex-proof system was a serious undertaking and required ANYbotics to completely rethink how they develop robots.

Meet the engineers behind ANYmal X as they share the journey of developing an Ex-proof inspection robot. ANYmal X redefines the robotics landscape by merging the most advanced robotics technologies with the highest industrial safety requirements.

 

[Administrator]

Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning

Premiered Mar 22, 2022

Nvidia presented parts of this work at GTC 2022, revealing our humanoid-quadruped transformer!

Title: Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning
Authors: Eric Vollenweider, Marko Bjelonic, Victor Klemm, Nikita Rudin, Joonho Lee and Marco Hutter
Paper submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems in Kyoto.

Abstract: In recent years, reinforcement learning (RL) has shown outstanding performance for locomotion control of highly articulated robotic systems. Such approaches typically involve tedious reward function tuning to achieve the desired motion style. Imitation learning approaches such as adversarial motion priors aim to reduce this problem by encouraging a pre-defined motion style. In this work, we present an approach to augment the concept of adversarial motion prior-based RL to allow for multiple, discretely switchable styles. We show that multiple styles and skills can be learned simultaneously without notable performance differences, even in combination with motion data-free skills. Our approach is validated in several real-world experiments with a wheeled-legged quadruped robot showing skills learned from existing RL controllers and trajectory optimization, such as ducking and walking, and novel skills such as switching between a quadrupedal and humanoid configuration. For the latter skill, the robot is required to stand up, navigate on two wheels, and sit down. Instead of tuning the sit-down motion, we verify that a reverse playback of the stand-up movement helps the robot discover feasible sit-down behaviors and avoids tedious reward function tuning.

Note: The following parts of the video are sped up:
- Door opening and closing when the robot stands inside the elevator between 00:20 and 00:21 (+200%)
- In-between the standing up and sitting down sequence between 00:49 and 01:10 (+200% only the navigation on two legs)
- Reaction with the crowd between 1:41 and 1:50 (+150%)
- Last drone footage after 2:01 (+200%)

Learn more at Swiss-Mile Robotics AG

 

[Administrator]

Rocky VI: robot training montage

May 24, 2022

Finally, after the first Rocky movie in 1976, the Robotic Systems Lab presents a continuation of the iconic series. Our transformer robot visited Philly in 2022 as part of the International Conference on Robotics and Automation.

Credits: Marko Bjelonic, Hendrik Kolvenbach, Takahiro Miki, Nikita Rudin, Vassilios Tsounis, Maria Vittoria Minniti, Julian Nubert, Lorenz Wellhausen, Jonhoo Lee, Dimitrios Kanoulas, Marco Hutter

 

[Administrator]

Robot loco-manipulation for industrial applications

Jun 27, 2022

This video shows uninterrupted operation of ANYmal Bull. The robot performs the following tasks: turning a wheel, pulling a lever, pushing a gate, and pulling a rope to lift a bucket. The robot walks around and up the ramp teleoperated via a remote controller. However, the robot solves each of the manipulation tasks autonomously.

Additional notes: Perception is done with a Vicon motion capture system and with the readings of the robot joint states. The bucket weighs approximately 1.1 kg.

Timestamps:
00:00 Turning the wheel
00:20 Pulling the lever
00:38 Walking around the scaffolding
00:55 Walking up the first ramp
01:12 Opening the gate
01:30 Walking to the top level
01:57 Lifting the bucket

 

[Administrator]

Perceptive Locomotion through Nonlinear Model Predictive Control

Aug 18, 2022

This work is currently under review, a preprint is available:
researchgate.net/publication/362759412_Perceptive_Locomotion_through_Nonlinear_Model_Predictive_Control

Title:
Perceptive Locomotion through Nonlinear Model Predictive Control

Authors:
Ruben Grandia, Fabian Jenelten, Shaohui Yang, Farbod Farshidian, and Marco Hutter

Abstract:
Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pipeline, that can optimize motions for all degrees of freedom of the robot in real-time. To mitigate the numerical challenges posed by the terrain a sequence of convex inequality constraints is extracted as local approximations of foothold feasibility and embedded into an online model predictive controller. Steppability classification, plane segmentation, and a signed distance field are precomputed per elevation map to minimize the computational effort during the optimization. A combination of multiple-shooting, real-time iteration, and a filter-based line-search are used to solve the formulated problem reliably and at high rate. We validate the proposed method in scenarios with gaps, slopes, and stepping stones in simulation and experimentally on the ANYmal quadruped platform, resulting in state-of-the-art dynamic climbing.

 

[Administrator]

Advanced skills by learning locomotion and local navigation end-to-end

Sep 27, 2022

Local navigation and locomotion of legged robots are commonly split into separate modules.
In this work, we propose to combine them by training an end-to-end policy with deep reinforcement learning.
Training a policy in this way opens up a larger set of possible solutions, which allows the robot to learn more complex behaviors.

IROS 2022
work by Nikita Rudin, David Hoeller, Marko Bjelonic, and Marco Hutter
paper: https://arxiv.org/abs/2209.12827
project website: sites.google.com/leggedrobotics.com/end-to-end-loco-navigation/home

 

[Administrator]

Slippery surfaces are no match for ANYmal

Mar 14, 2023

 

[Administrator]

ANYmal plowing through belly-deep snow

Mar 15, 2023

 

[Administrator]

Towards legged locomotion on steep planetary terrain

Aug 2, 2023

We present a novel locomotion strategy for the legged robotic platform ANYmal. The paper will be presented at the IROS 2023 conference in Detroit, USA.

Paper: research-collection.ethz.ch/handle/20.500.11850/625001
Authors: Cedric Weibel, Giorgio Valsecchi, Hendrik Kolvenbach, Marco Hutter

Credits: ETH Zurich, Beyond Gravity, ANYmal Research, ESA
Music: Cinematic Atmosphere Score 2, Resistance - GvidonAudio

 

[Administrator]

ANYmal mastering the uneven wooden obstacle at SPRINT Robotics World Conference 2023

Oct 18, 2023

ANYmal, the autonomous legged robot designed for industrial challenging environments, provides the mobility, autonomy, and inspection intelligence to enable safe and efficient inspection operations. In this virtual showcase, discover how ANYmal climbs stairs, recovers from a fall, performs an autonomous mission and avoids obstacles, docks to charge by itself, digitizes analog sensors, and monitors the environment.

 

[Administrator]

The unrivaled performance of ANYmal: 24/7 in all weather

Dec 7, 2023

ANYmal, the autonomous legged robot designed for industrial challenging environments, provides the mobility, autonomy, and inspection intelligence to enable safe and efficient inspection operations. In this virtual showcase, discover how ANYmal climbs stairs, recovers from a fall, performs an autonomous mission and avoids obstacles, docks to charge by itself, digitizes analog sensors, and monitors the environment.