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Thread: Miscellaneous

  1. #11


    Studying the jerboa to advance bipedal robots

    Published on Sep 5, 2017

    Researchers from the University of Michigan have created a model to quantitatively measure the unpredictability of the movement pattern of the jerboa, a bipedal desert rodent. Ram Vasudevan, an assistant professor in mechanical engineering, in partnership with Talia Moore, a research fellow in ecology and evolutionary biology, used Information Theory to measure the randomness or unpredictability of this highly evasive animal. The researchers believe this new model for unpredictable movement can be applied to bipedal robots as a way to engineer unpredictability in their gait.
    "How a hopping mouse and information theory could inform robotic locomotion"

    by Ram Vasudevan
    September 5, 2017

  2. #12


    Walking Robot Que-Kaku driven by pneumatic artificial muscles

    Published on Jan 11, 2018

  3. #13


    Prof. Dennis Hong Sunum

    Published on Nov 23, 2018

  4. #14


    Hands-on / Off Campus: Robotics Studio

    Jan 22, 2021

    In spite of the pandemic, Professor Hod Lipson’s Robotics Studio persevered and even thrived— learning to work on global teams, to develop protocols for sharing blueprints and code, and to test, evaluate, and refine their designs remotely. Equipped with a 3D printer and a kit of electronics prototyping equipment, our students engineered bipedal robots that were conceptualized, fabricated, programmed, and endlessly iterated around the globe in bedrooms, kitchens, backyards, and any other makeshift laboratory you can imagine.

  5. #15


    Meet BirdBot, an energy-efficient robot leg - research published in Science Robotics

    Mar 16, 2022

    A team of scientists at the Max Planck Institute for Intelligent Systems and the University of California, Irvine constructed a robot leg that, like its natural model, is very energy efficient. BirdBot benefits from a foot-leg coupling through a network of muscles and tendons that extends across multiple joints. In this way, BirdBot needs fewer motors than previous legged robots and could, theoretically, scale to large size. On March 16th, the researchers will publish their work in Science Robotics.
    is.mpg.de/news/birdbot-is-energy-efficient-thanks-to-nature-as-a-model

  6. #16


    Large-scale biped robot using hybrid leg mechanism

    May 12, 2022

    Implementation of a Large-scale Biped Robot Using Serial-Parallel Hybrid Leg Mechanism

    By Kevin G Gim and Joohyung Kim

    This paper presents our implementation of a large-scale biped robot utilizing Hybrid Leg, a 6 DoF serial-parallel mechanism, having lightweight structure, high payload and large workspace. We set our design goal to make a biped robot taller than an average human height. By applying the Hybrid mechanism and design optimization, the robot was built with a height of 1.84m and a weight of 29.05kg. The implemented robot is able to be actuated by the servo motors used in the smaller humanoid robot. The mechanical design of the robot is explained in detail and kinematics analysis is conducted for analytical solutions. Through multi-body dynamics simulations, the proposed robot design and its performance are verified. In addition, the preliminary performance evaluations for the robot hardware are conducted for a squat experiment and in-place walking experiment.
    publish.illinois.edu/kimlab2020

  7. #17


    Learning bipedal walking for humanoids with current feedback

    Mar 7, 2023

    github.com/rohanpsingh/LearningHumanoidWalking

  8. #18


    Learning humanoid locomotion with transformers

    Mar 7, 2023

    We present a sim-to-real learning-based approach for real-world humanoid locomotion. Our controller is a causal Transformer trained by autoregressive prediction of future actions from the history of observations and actions. We hypothesize that the observation-action history contains useful information about the world that a powerful Transformer model can use to adapt its behavior in-context, without updating its weights. We do not use state estimation, dynamics models, trajectory optimization, reference trajectories, or pre-computed gait libraries. Our controller is trained with large-scale model-free reinforcement learning on an ensemble of randomized environments in simulation and deployed to the real world in a zero-shot fashion. We evaluate our approach in high-fidelity simulation and successfully deploy it to the real robot as well. To the best of our knowledge, this is the first demonstration of a fully learning-based method for real-world full-sized humanoid locomotion.
    "Learning Humanoid Locomotion with Transformers"

    by Ilija Radosavovic, Tete Xiao, Bike Zhang, Trevor Darrell, Jitendra Malik, Koushil Sreenath
    March 6, 2023

  9. #19


    Storytelling Through Characters at Disney Parks I SXSW 2023

    Mar 11, 2023

    The ‘Art & Science’ of storytelling is the secret to how we amaze our guests and delivers memorable experiences.  Check out this video as Disney Parks, Experiences & Products Chairman Josh D’Amaro shares how storytelling techniques will build on our legacy of creativity and innovation for a world that can always use just a little more happiness.

    00:00 Intro
    0:32 Adventure to Distant Lands – Tinker Bell
    04:17 Using robotics to create characters
    "Disney Robot Debuts at SXSW"
    The robot, modeled after bunny rabbit character Judy Hopps, somersaulted and rollerbladed around the stage

    by Scarlett Evans
    March 13, 2023

    Disney Research
    Last edited by Airicist2; 18th March 2023 at 12:13.

  10. #20


    Walk improvements - Falling disabled

    Apr 2, 2023

    Our new walk is based on our 2022 walk. To ensure stability, we use a regulation to modify the allowed rotation speed of the support foot’s joints. Thus, the different leg parts will still execute the intended motion, but based on the center of mass and the measured rotation errors of the support foot, some leg parts are slowed down if needed.

    Additionally, to handle more extreme cases at higher walking speeds, a neural network is used to predict future joint position measurements to calculate future position errors.

    The robots are now able to handle more difficult situations. Also, as an unintended effect, the robots lift up on the tip of the supporting foot, just like humans do.

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