Article "Agility Robotics' Cassie Is Now Astonishingly Good at Stairs"
Using only proprioceptive sensors, this bipedal robot is probably better at stairs than you are
by Evan Ackerman
May 20, 2021
Article "Agility Robotics' Cassie Is Now Astonishingly Good at Stairs"
Using only proprioceptive sensors, this bipedal robot is probably better at stairs than you are
by Evan Ackerman
May 20, 2021
https://youtu.be/gE3Y-2Q3gco
Fully autonomy on the Wave Field 2021
Jun 4, 2021
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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 The planner is currently unpublished.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://youtu.be/FSaSjd_HOaI
Jul 27, 2021
https://youtu.be/dY57qnD_O7UQuote:
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.
Machine learning breakthrough: Robot runs a 5k
Jul 27, 2021
"Cassie the bipedal robot runs a 5K"Quote:
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.
by Brian Heater
July 28, 2021
https://youtu.be/utWqXZwTIbQ
Terrain-Aware Foot Placement for Bipedal Locomotion Combining MPC, Virtual Constraints, and the ALIP
Nov 4, 2021
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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.
https://youtu.be/3HVJotA-w4Y
Cassie autonomously navigates around obstacles
Nov 17, 2021
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Cassie Blue navigates around furniture treated as obstacles in the atrium of the Ford Robotics Building at the University of Michigan.
https://youtu.be/PT2mVaKTdT8
Cassie autonomously navigates in four long corridors (200 meters)
Nov 29, 2021
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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.
https://youtu.be/AmPvQMpIHSw
Terrain - Adaptive, ALIP-based bipedal locomotion controller via MPC and virtual constraints - extended
Jul 30, 2022
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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.
https://youtu.be/DdojWYOK0Nc
Cassie sets world record for 100M run
Sep 27, 2022
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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.
https://youtu.be/utANK8jTwuI?si=g1cnFiquzayhTFxk
Cassie on a Moving Walkway
Nov 21, 2023
https://youtu.be/pKNiDnennBM?si=43-UICQG3p_1UyMp
Cassie walks on sand, gravel, and rocks in the Robot Playground
Apr 1, 2024
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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