Cassie, bipedal robot, Dynamic Robotics Laboratory, Corvallis, Oregon, USA [Archive] - Forums - Portal of Robotics and Artificial Intelligence

Airicist

6th September 2017, 14:18

https://youtu.be/teCBAppDdL8

Cassie's arrival | Michigan Robotics

Published on Sep 6, 2017

Michigan Robotics’ latest member arrived last week in a black plastic case about two feet to a side, buried beneath a layer of foam cubes and crouched on a metal calibration stand.
The latest model, dubbed Cassie Blue by the Michigan team, has control over two more joints in each leg – motors for hip rotation and at the ankle for extra stability. Not only do these give Cassie the potential to be better at the independent walking pioneered by its predecessor, MARLO, but it opens a host of new possibilities.
Jessy Grizzle, the director of Michigan Robotics and the Elmer G. Gilbert Distinguished University Professor of Engineering, is especially interested in mounting a camera on Cassie and incorporating fast image processing on its extra chip, enabling the robot to use video to identify large changes in terrain visually.
This, Grizzle says, will prepare Cassie for Michigan’s “robot torture track” – the art installation known as the Wave Field. The field of three-foot earthen mounds totally destroyed one of MARLO’s knees last summer when the robot stepped blindly from the top of one hillock into the ditch.
“You’ve seen kids running freely over the Wave Field?” Grizzle asked. “That’s what this robot should be able to do.”

Airicist

6th September 2017, 20:35

https://youtu.be/DS18SeuMqtA

Cassies take a tour of Agility Robotics

Published on Sep 6, 2017

Two Cassies decide to take a walking tour of our office. No CG: 100% actual robots.

Airicist

30th October 2017, 12:02

https://youtu.be/YBVcHJdHoNs

Cassie Blue walking in the rain

Published on Oct 30, 2017

Cassie Blue is walking with control laws developed at Michigan.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI).

Airicist

31st October 2017, 01:17

https://youtu.be/bioubaR40C8

Cassie Blue's first outdoor adventure

Published on Oct 30, 2017

Don't miss the bloopers at the end!

Cassie Blue is walking with control laws developed at Michigan.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

31st October 2017, 01:18

https://youtu.be/h6hnfCo4a00

The original record of Cassie's first outdoor experiment

Published on Oct 30, 2017

Cassie Blue is walking with control laws developed at Michigan.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

16th November 2017, 22:45

https://youtu.be/onYv7mfHo9k

Who says Cassie Blue is afraid of the dark?

Published on Nov 16, 2017

Cassie Blue is walking with control laws developed at Michigan.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

8th December 2017, 10:44

https://youtu.be/ZL8ddCQFuXU

Toward the Robots of Science Fiction - A. Ames - 12/6/2017

Published on Dec 7, 2017

"Toward the Robots of Science Fiction, " by Aaron D. Ames, Bren Professor of Mechanical and Civil Engineering and Control and Dynamical Systems, Caltech

Science fiction has long promised a world of robotic possibilities: from humanoid robots in our everyday lives, to wearable robotic devices that restore and augment human capabilities, to swarms of autonomous robotic systems forming the backbone of the cities of the future, to robots enabling exploration of the cosmos.

Achieving the promise of science fiction will require imbuing machines with the dynamic locomotion behaviors that humans display with deceptive ease—navigating everything from daily environments to uneven and uncertain terrain with efficiency and robustness.

This talk will present the first steps toward achieving this goal on bipedal and humanoid robots with the result being dynamic and efficient locomotion displaying the hallmarks of natural human walking. The translation of these ideas to robotic assistive devices along with a wide range of safety-critical systems will be demonstrated with a view toward realizing the robots of science fiction.

Airicist

15th December 2017, 21:53

https://youtu.be/jhQMq6vpnAo

Cassie Blue Tours the World's #1 Ranked Dental School

Published on Dec 15, 2017

Cassie Blue is walking with control laws developed at Michigan in this publication: Da, X., Harib, O., Hartley, R., Griffin, B., & Grizzle, J. W. (2016). From 2D design of underactuated bipedal gaits to 3D implementation: Walking with speed tracking. IEEE Access, 4, 3469-3478.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

22nd March 2018, 22:21

https://youtu.be/f77NAz0HitI

Robot playdate

Published on Mar 22, 2018

Cassie had a meet-and-greet with a four-legged friend during one of our visits to Playground.

Airicist

8th April 2018, 20:06

https://youtu.be/ugEu0hzC8Xg

Robot on a Segway - a teasser of more to come

Published on Apr 8, 2018

Cassie Blue was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The robot has been operating Since October 2017 with control laws designed at Michigan and funded by NSF Grant NRI-1525006.

Airicist

24th April 2018, 21:10

https://youtu.be/cGb3bE6ZwrQ

Cassie Blue walks through fire

Published on Apr 24, 2018

The prescribed burn shown in the video took place on April 21, 2018 on the North Campus of the University of Michigan. The burns are done to promote the ecology of native species; you can learn more here "Controlled Burns (https://www.a2gov.org/departments/Parks-Recreation/NAP/Pages/PrescribedEcologicalBurns.aspx)"

Robots can be protected against flames and they do not suffer from smoke inhalation. While Cassie Blue has no arms to carry you out, coming generations of robots will be able to assist you.

Cassie Blue is walking with control laws developed at Michigan in this publication: Da, X., Harib, O., Hartley, R., Griffin, B., & Grizzle, J. W. (2016). From 2D design of underactuated bipedal gaits to 3D implementation: Walking with speed tracking. IEEE Access, 4, 3469-3478.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

11th May 2018, 13:38

https://youtu.be/0gauVSUJzd0

Cassie Blue рones рer Segway riding skills

Published on May 11, 2018

Cassie Blue is controlling the motion of the Segway by body lean, just as a human rider would do. To turn, she leans into the middle bar of the Segway with her "shin".

The feedback law being used is Cassie Blue's normal standing controller. Yukai Gong, via the remote controller, commands Cassie Blue's body lean. It would be much better if we modified the controller system so that we just sent desired speed and turn radius to the robot, and let feedback take care of the rest! Because we did not do that, we ran into a bit of trouble: the Segway has a built-in speed limiter. We'd be commanding Cassie Blue to go faster, faster, faster, by leaning further and further forward, and when the Segway would hit is speed limit, Cassie Blue would simply tip forward and fall of the Segway!

Airicist

11th May 2018, 13:39

https://youtu.be/VoD7hbssu-M

Cassie Blue plays in the sand

Published on May 11, 2018

Cassie Blue is operating with our normal flat-ground feedback controller. We made no changes to accommodate the sand. We were quite surprised that she could walk barefoot in such soft sand! Our best guess is that this ability is a fortunate outcome of the way we control her "feet". When a leg is in the air, we control the corresponding foot to be level with the ground. When a leg is in contact with the ground, we set the "ankle" torque to zero. In other words, we are treating the robot as being underactuated.

The Discovery Channel was filming on May 9, 2018.

Cassie Blue is walking with control laws developed at Michigan in this publication: Da, X., Harib, O., Hartley, R., Griffin, B., & Grizzle, J. W. (2016). From 2D design of underactuated bipedal gaits to 3D implementation: Walking with speed tracking. IEEE Access, 4, 3469-3478.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

24th May 2018, 19:00

https://youtu.be/rfrHctCH2zw

Cassie on stairs: forwards, backwards, and sideways

Published on May 24, 2018

Cassie shows off some new abilities, and - bonus! - avoids crashing through the nearby window.

Airicist

24th May 2018, 19:01

https://youtu.be/_6XBZHvt7bk

Cassie: walk in the park

Published on May 24, 2018

Practicing inside is all well and good, but nothing beats a walk outdoors on a spring day.

Airicist

24th May 2018, 19:03

https://youtu.be/svwQ4a5L1tk

Bipedal Robot in Wildfire Awareness Report, Action News Now, Chico-Redding, California

Published on May 24, 2018

Video posted with permission:
Spencer Joseph, Reporter
Lorraine Dechter, Assignment Desk/Producer
Action News Now, Chico-Redding, CA

Cassie Blue is walking with control laws developed at Michigan in this publication: Da, X., Harib, O., Hartley, R., Griffin, B., & Grizzle, J. W. (2016). From 2D design of underactuated bipedal gaits to 3D implementation: Walking with speed tracking. IEEE Access, 4, 3469-3478.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

24th May 2018, 21:38

https://youtu.be/XoY5vl3rQR8

Trolling Agility Robotics (or, We Love Your Bipedal Robot!)

Published on May 24, 2018

We have been working toward a principled approach to stair climbing when we saw Agility Robotics tweet on 25 May 2018 involving their Cassie walking on platforms. We could not resist posting what we have so far as well. Keep building those awesome robots!

Cassie Blue is walking with control laws developed at Michigan in this publication: Da, X., Harib, O., Hartley, R., Griffin, B., & Grizzle, J. W. (2016). From 2D design of underactuated bipedal gaits to 3D implementation: Walking with speed tracking. IEEE Access, 4, 3469-3478.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

25th May 2018, 12:05

https://youtu.be/oyKCBc9CZUA

Bipedal Robot Cassie Blue's Clutzy Operators and a Segway: Bloopers

Published on May 25, 2018

Operator Error #1: Gantry hit the sidewalk and stopped, pulling Cassie Blue off the Segway.

Operator Error #2: Bruce thought we could go through the wall in some quantum mechanics trick!

Controller Error #3: Bad interactions between the Segway's control loop and Cassie Blue's controller. That is called instability!

Controller Error #4: See above!

Operator Error #5: Yukai commanded too big of a lean!

Segway Error #6: The Segway hit its internal speed limit and leaned backward to slow down without asking permission first from Cassie Blue, the rider. That was a really bad idea!

Cassie Blue is operating with control laws developed at Michigan in this publication: Da, X., Harib, O., Hartley, R., Griffin, B., & Grizzle, J. W. (2016). From 2D design of underactuated bipedal gaits to 3D implementation: Walking with speed tracking. IEEE Access, 4, 3469-3478.

Cassie was built by Agility Robotics. The robot's purchase was enabled by funding from NSF Inspire Grant ECCS-1343720 and Toyota Research Institute (TRI). The work on the control law was funded by NSF Grant NRI-1525006.

Airicist

15th June 2018, 15:21

https://youtu.be/av8xjJYQsvE

Cassie vs scattered boards

Published on Jun 15, 2018

Cassie is an efficient, compliant, dynamic bipedal robot. Here in the Dynamic Robotics Lab directed by Dr. Jonathan Hurst we are researching control strategies that exploit the robot's natural dynamics. The controller here is demonstrating robust blind walking over unperceived disturbances.

Funded by
DARPA Award WN911NF-16-1-0002
National Science Foundation Graduate Research Fellowship Grant No. 1314109-DGE

Thanks to Agility Robotics for designing and producing Cassie.

Airicist

20th September 2018, 11:44

https://youtu.be/UhXly-5tEkc

Feedback control of a Cassie bipedal robot: walking, standing, and riding a Segway

Published on Sep 20, 2018

Airicist

30th January 2019, 23:21

https://youtu.be/oGxsbborgQg

Bipedal Robot Cassie Blue and Maize vs the Polar Vortex!

Published on Jan 30, 2019

30 January 2019: We wanted to see how long Cassie could operate during the "polar vortex" that has shutdown the campus! Well, she did one hour and two minutes of continuous operation at -22 C (-8 F).

Airicist

20th February 2019, 22:01

https://youtu.be/qV-92Bq96Co

Cassie: dynamic planning on stairs

Published on Feb 20, 2019

This video shows the progression of increasingly complex gait strategies from ATRIAS through recent results with Cassie. Specifically, Cassie's controller now includes planned footstep placements in addition to dynamic balancing, allowing access to substantially more complicated terrains.

Airicist

24th April 2019, 01:24

https://youtu.be/NYooAyAC0kA

Cassie: dynamic walking with compliance

Published on Apr 23, 2019

Dynamic walking with Cassie, from simulation to outdoor experiments, utilizing the full dynamics of the robot to generate the walking gait. This includes leverage the compliance, i.e., springs, to achieve the desired walking gaits.

Airicist

11th May 2019, 19:52

https://youtu.be/TgFrcrARao0

Iterative gait design for Cassie (https://arxiv.org/abs/1903.09537)

Published on Apr 26, 2019

Results of using DeepRL on a Cassie robot. Policies are trained in simulation and directly applied on hardware.

Airicist

4th July 2019, 11:04

https://youtu.be/f-FvcHOQXPc

Cassie vs Spin: a true story

Published on Jul 4, 2019

Cassie Blue gets her independence on July 4th! In this video, Cassie Blue is navigating autonomously. Right now, her world is very small, the Wavefield at the University of Michigan, where she is told to turn left at any intersection. You're right, that is not a lot of independence, but it's a first step away from a human and an RC controller!

Using a RealSense RGBD Camera, Cassie Blue is building a 3D semantic map in real time that identifies sidewalks, grass, poles, bicycles, and buildings. From the semantic map, occupancy and cost maps are built with the sidewalk identified as walk-able area and everything else considered as an obstacle. A planner then sets a goal to stay approximately 50 cm to the left of the sidewalk's edge and plans path around obstacles and corners. The path is translated into waypoints that are achieved via Cassie Blue's gait controller.

While there is a lot left to do, we knew that Cassie Blue would enjoy celebrating Independence Day with you!

Airicist

11th July 2019, 20:53

https://youtu.be/CBdDcsb1DWM

Published on Jul 11, 2019

Cassie (30 Kg) is walking at a comfortable speed for human while carrying her (9 Kg) perception system. The original controller is used, with the definition of the virtual leg modified to be the line segment connecting overall Center of Mass to her toe. This moves the "control point" up about 10 cm.

Airicist

2nd April 2020, 21:00

https://youtu.be/0nM7lml7kiE

Learning spring mass locomotion on Cassie

Apr 2, 2020

This is the video that accompanies our submission to the 2020 RSS conference which is currently under review. Once the review process is complete, we will post our full text preprint on an open access site and add a link here. The full title is:

Learning Spring Mass Locomotion: Guiding Policies with a Reduced Order Model
Kevin Green, Yesh Godse, Jeremy Dao, Ross L. Hatton, Alan Fern and Jonathan Hurst

Airicist

24th June 2020, 21:59

https://youtu.be/fMKVB822dOw

Cassie Blue doing her thing on the U. of Michigan North Campus lawn

Jun 24, 2020

Airicist

25th June 2020, 15:22

https://youtu.be/nRYXianfoQk

Cassie vs the hill

Jun 25, 2020

The controller is designed for flat ground and treats the 22 degree slope as a disturbance. As you can "see'', Cassie is not equipped with a vision system in this video. This is called "blind walking", though, to be completely fair, the operator does have vision and it's hard to know if he is making subtle corrections over the RC controller. He says not....

Airicist

2nd July 2020, 20:59

https://youtu.be/-35xJfFMDkE

Cassie Blue goes a ridge too far

Jul 2, 2020

As our control skills increase, we are more and more impressed by what a Cassie bipedal robot can do. Those who have been following our channel, know that we always shows the limitations of our work. So while there is still much to do, you gotta like the direction things are going. Later this year, you will see this controller integrated with our real-time planner and perception system. Autonomy with agility! Watch out for us!

Valley 1: 0:00
Valley 2: 1:11
Crossing Ridges 1: 2:03
Crossing Ridges 2: 2:43

Airicist

21st July 2020, 21:16

https://youtu.be/lu_jcL84wNE

Bipedal robot Cassie Blues reaches a walking speed of 2.1m/s

Jul 21, 2020

We are using distance traveled divided by time to estimate the velocity. The distance traveled is marked at time 0:39 of the video; see also here (https://youtu.be/lu_jcL84wNE?t=39). The plot shown at time 0.37 (https://youtu.be/lu_jcL84wNE) is an independent estimate from a Kalman Filter. When we get a chance, we'll try the same experiment on a running track without the gantry.

Can Cassie walk faster? Probably not, without running!

Airicist

25th October 2020, 21:18

https://youtu.be/Wb0tIWBrjmc

Cassie robot learns to hop, run and skip

Oct 25, 2020

These are some preliminary results of our lab's new work on using reinforcement learning to train neural networks to imitate common bipedal gait behaviors, without using any motion capture data or reference trajectories. Our method is described in an upcoming submission to ICRA 2021. Work by Jonah Siekmann and Yesh Godse.

Airicist

10th April 2021, 03:10

Article "Forget Boston Dynamics. This robot taught itself to walk (https://www.technologyreview.com/2021/04/08/1022176/boston-dynamics-cassie-robot-walk-reinforcement-learning-ai)"
Slick, viral videos from Boston Dynamics are impressive but teaching a robot to walk by itself is a lot harder.

by Will Douglas (https://www.linkedin.com/in/will-douglas-heaven-843358b)
April 8, 2021

Airicist

19th May 2021, 03:10

https://youtu.be/MPhEmC6b6XU

Robot learns to climb stairs blind

May 19, 2021

We successfully used reinforcement learning to train a recurrent neural network to control our bipedal robot Cassie to climb stairs without any perception sensors such as LIDAR or cameras.

This video accompanies our submission to the 2021 Robotics: Science and System conference. A preprint of the full paper can be found here https://arxiv.org/abs/2105.08328.

"Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning" Jonah Siekmann, Kevin Green, John Warila, Alan Fern, Jonathan Hurst

Airicist

19th May 2021, 03:11

https://youtu.be/nuhHiKEtaZQ

Reliability tests of a learned stair climbing controller

May 19, 2021

To show how reliable our learned controller is, we wanted to publish an uninterrupted video of ten stair ascents and ten stair descents. The controller being tested is described in our 2021 Robotics: Science and Systems paper "Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning" Preprint: https://arxiv.org/abs/2105.08328

Airicist

23rd May 2021, 09:07

Article "Agility Robotics' Cassie Is Now Astonishingly Good at Stairs (https://spectrum.ieee.org/automaton/robotics/humanoids/agility-robotics-cassie-stairs)"
Using only proprioceptive sensors, this bipedal robot is probably better at stairs than you are

by Evan Ackerman (https://www.linkedin.com/in/evan-ackerman-31112216)
May 20, 2021

Airicist

14th July 2021, 19:04

https://youtu.be/gE3Y-2Q3gco

Fully autonomy on the Wave Field 2021

Jun 4, 2021

During the dark of night, using LiDAR for eyes, Cassie Blue is operating fully autonomously on the University of Michigan Wave Field. The terrain is challenging and was not pre-mapped.

The LiDAR and IMU data are fused in real-time to form an elevation map. A CLF- RRT* planner is running at 5Hz with reactive re-planning performed at 300 Hz on the basis of a Control Lyapunov Function (CLF). The reactive planner provides velocity commands to a One-step Ahead Gait Controller based on Angular Momentum https://youtu.be/V36DCsc6iio . The paper https://arxiv.org/abs/2105.08170 provides full details on the controller. The planner is currently unpublished.

This work was presented at the 5th ICRA Workshop on Legged Robotics on 4 June 2021.
docs.google.com/presentation/d/1onC0VqRHnzqb3jgufmPRgeIwla74jQLFmeLGrjASdRw/edit#slide=id.g4cee01c7e5_0_24 (https://docs.google.com/presentation/d/1onC0VqRHnzqb3jgufmPRgeIwla74jQLFmeLGrjASdRw/edit#slide=id.g4cee01c7e5_0_24)

Airicist

28th July 2021, 15:29

https://youtu.be/FSaSjd_HOaI

Jul 27, 2021

To encourage us to push the limits of reliability, energy efficiency and speed we attempted to run a 5k (5 kilometers, 3.1 miles) with our bipedal robot Cassie.

Our final goal was to run a 5k through the Oregon State University campus. That can be seen here https://youtu.be/dY57qnD_O7U

This turf 5k was a test before we attempted the campus run. We finished with a time of 43:59.53 which means we had an average speed of 1.89 m/s. This is the fastest bipedal robot 5k at the time of posting.

https://youtu.be/dY57qnD_O7U

Machine learning breakthrough: Robot runs a 5k

Jul 27, 2021

The OSU Dynamic Robotics Laboratory's research team, led by Agility Robotics’ Jonathan Hurst, combined expertise from biomechanics and robot controls with new machine learning tools to accomplish something new: train a bipedal robot to run a full 5K on a single battery charge! This industry-first invention will unleash new levels of robot performance. Today, some of these same researchers are Agility Robotics employees busily applying their know-how to Digit, so just wait to see what we have in store.

"Cassie the bipedal robot runs a 5K (https://techcrunch.com/2021/07/27/cassie-the-bipedal-robot-runs-a-5k)"

by Brian Heater (https://www.linkedin.com/in/brian-heater-094a921)
July 28, 2021

Airicist2

7th November 2021, 11:23

https://youtu.be/utWqXZwTIbQ

Terrain-Aware Foot Placement for Bipedal Locomotion Combining MPC, Virtual Constraints, and the ALIP

Nov 4, 2021

Title: Terrain-Aware Foot Placement for Bipedal Locomotion Combining Model Predictive Control, Virtual Constraints, and the ALIP

Authors: Grant Gibson, Oluwami Dosunmu-Ogunbi, Yukai Gong, Jessy Grizzle

Preprint link: https://arxiv.org/abs/2109.14862

Abstract:
This paper draws upon three themes in the bipedal control literature to achieve highly agile, terrain-aware locomotion. By terrain aware, we mean the robot can use information on terrain slope and friction cone as supplied by state-of-the-art mapping and trajectory planning algorithms. The process starts with abstracting from the full dynamics of a Cassie 3D bipedal robot, an exact low-dimensional representation of its centroidal dynamics, parameterized by angular momentum. Under a piecewise planar terrain assumption, and the elimination of terms for the angular momentum about the robot's center of mass, the centroidal dynamics become linear and has dimension four. Four-step-horizon model predictive control (MPC) of the centroidal dynamics provides step-to-step foot placement commands. Importantly, we also include the intra-step dynamics at 10 ms intervals so that realistic terrain-aware constraints on robot's evolution can be imposed in the MPC formulation. The output of the MPC is directly implemented on Cassie through the method of virtual constraints. In experiments, we validate the performance of our control strategy for the robot on inclined and stationary terrain, both indoors on a treadmill and outdoors on a hill.

We thank Margaret Eva Mungai, Jennifer Humanchuk, and Jianyang Tang for assistance in experiments.

Airicist2

21st November 2021, 23:41

https://youtu.be/3HVJotA-w4Y

Cassie autonomously navigates around obstacles

Nov 17, 2021

Cassie Blue navigates around furniture treated as obstacles in the atrium of the Ford Robotics Building at the University of Michigan.

Airicist2

13th December 2021, 17:09

https://youtu.be/PT2mVaKTdT8

Cassie autonomously navigates in four long corridors (200 meters)

Nov 29, 2021

Cassie Blue autonomously navigates on the second floor of the Ford Robotics Building at the University of Michigan. The total traverse distance is 200 m (656.168 feet).

The LiDAR and IMU data are fused in real-time to form an elevation map. The system consists of a low-frequency planning thread (5 Hz) to find an asymptotically optimal path and a high-frequency reactive thread (300 Hz) to accommodate robot deviation. The planning thread includes: a multi-layer local map to compute traversability for the robot on the terrain; an anytime omnidirectional Control Lyapunov Function (CLF) for use with a Rapidly Exploring Random Tree Star (RRT*) that generates a vector field for specifying motion between nodes; a sub-goal finder when the final goal is outside of the current map; and a finite-state machine to handle high-level mission decisions. The paper [ https://arxiv.org/abs/2108.06699 ] provides full details on the reactive planning system.

Airicist2

5th August 2022, 23:09

https://youtu.be/AmPvQMpIHSw

Terrain - Adaptive, ALIP-based bipedal locomotion controller via MPC and virtual constraints - extended

Jul 30, 2022

Extended Video for IROS 2022 Accepted Paper (https://arxiv.org/abs/2109.14862)

Authors: Grant Gibson, Oluwami Dosunmu-Ogunbi, Yukai Gong, and Jessy Grizzle

Title: Terrain-Adaptive, ALIP-Based Bipedal Locomotion Controller via Model Predictive Control and Virtual Constraints

Abstract:
This paper presents a gait controller for bipedal robots to achieve highly agile walking over various terrains given local slope and friction cone information. Without these considerations, untimely impacts can cause a robot to trip and inadequate tangential reaction forces at the stance foot can cause slippages. We address these challenges by combining, in a novel manner, a model based on an Angular Momentum Linear Inverted Pendulum (ALIP) and a Model Predictive Control (MPC) foot placement planner that is executed by the method of virtual constraints. The process starts with abstracting from the full dynamics of a Cassie 3D bipedal robot, an exact low-dimensional representation of its center of mass dynamics, parameterized by angular momentum. Under a piecewise planar terrain assumption and the elimination of terms for the angular momentum about the robot's center of mass, the centroidal dynamics about the contact point become linear and have dimension four. Importantly, we include the intra-step dynamics at uniformly-spaced intervals in the MPC formulation so that realistic workspace constraints on the robot's evolution can be imposed from step-to-step. The output of the low-dimensional MPC controller is directly implemented on a high-dimensional Cassie robot through the method of virtual constraints. In experiments, we validate the performance of our control strategy for the robot on a variety of surfaces with varied inclinations and textures.

Airicist2

1st October 2022, 01:51

https://youtu.be/DdojWYOK0Nc

Cassie sets world record for 100M run (https://today.oregonstate.edu/news/bipedal-robot-developed-oregon-state-achieves-guinness-world-record-100-meters)

Sep 27, 2022

Cassie, the robot, clocked the historic time of 24.73 seconds at the Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. This incredible achievement was accomplished through robot learning and almost a year of simulation, condensed down to a matter of weeks.

Cassie was invented at the Oregon State University College of Engineering and produced by Agility Robotics. Through this work Cassie has established a Guinness World Record for the fastest 100 meters by a bipedal robot.

Airicist2

25th November 2023, 16:27

https://youtu.be/utANK8jTwuI?si=g1cnFiquzayhTFxk

Cassie on a Moving Walkway

Nov 21, 2023

Airicist2

7th April 2024, 17:23

https://youtu.be/pKNiDnennBM?si=43-UICQG3p_1UyMp

Cassie walks on sand, gravel, and rocks in the Robot Playground

Apr 1, 2024

Using the controller from the paper below, Cassie is able to walk on sand, gravel, and rocks inside the Robot Playground at the University of Michigan

Paper: https://arxiv.org/abs/2403.02486

Airicist2

12th April 2024, 21:34

https://youtu.be/6kgB8PqvLYU?si=cDu8qiB3cg29Q-XJ

Learning vision-based bipedal locomotion for challenging terrain

Apr 6, 2024

Supplementary video for 2024 IEEE International Conference on Robotics and Automation
arXiv: https://arxiv.org/abs/2309.14594

Abstract - Reinforcement learning (RL) for bipedal locomotion has recently demonstrated robust gaits over moderate terrains using only proprioceptive sensing. However, such blind controllers will fail in environments where robots must anticipate and adapt to local terrain, which requires visual perception. In this paper, we propose a fully-learned system that allows bipedal robots to react to local terrain while maintaining commanded travel speed and direction. Our approach first trains a controller in simulation using a heightmap expressed in the robot's local frame. Next, data is collected in simulation to train a heightmap predictor, whose input is the history of depth images and robot states. We demonstrate that with appropriate domain randomization, this approach allows for successful sim-to-real transfer with no explicit pose estimation and no fine-tuning using real-world data. To the best of our knowledge, this is the first example of sim-to-real learning for vision-based bipedal locomotion over challenging terrains.