Miscellaneous

Arduino Quadruped Robot - Stalker
Detail: blog.oscarliang.net/arduino-quadruped-robot-stalker

What's new:
1. i have been researching how to produce smooth movement, realize input can play a very big part too.
2. It can now walk! the current gait is called creeping gait.

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The Stag is a quadruped robot under active development. Mark 2 is almost twice the size of its predecessor and now 100% more autonomous.

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This is my first robot.
Will update IR sensor and purchase Pin Li-Poly to complete this project.

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Here's a new Quadruped robot that we've designed from the ground up. This is the first prototype and we'll continue to develop further, eventually offering it in kit form and open sourcing the design once it's in a more refined state.

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This is a basic but stable gait used for testing my quadruped robot.

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A research team at east China's Shangdong University invented a four-legged robot that can walk like a quadripedal animal while bearing a load.

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Nice collaborative work within the AMARSi consortium.

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I know the gait isn't smooth. It has no IK ( Inverse Kinematics ), and the servos jitter too much. But this is my first microcontroller project. So this is it...

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This study shows some experiments to explain the physical aspect of the gait adjustment mechanism for quadrupeds in nature. The major contribution of this study was to show that 3D active walking with autonomous motion adjustment is possible without any explicit controller. Our robot can generate typical quadruped gaits and chooses a suitable one automatically with no sensor, no brain, no controller, only a purely physical mechanism. The key point of this approach is to exploit the weakness of a low-torque actuator. This passivity takes the function of a closed-loop controller and adjusts its own phases. This result leads a hypothesis: Animals may not use their brain to generate and choose a suitable gait, just like this robot. We envision this result will be a milestone to understand the amazing animal abilities.

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Authors: Jeff Duperret, Daniel E. Koditschek

Abstract:
We document empirically stable bounding using an actively powered spine on the Inu quadrupedal robot, and propose a reduced-order model to capture the dynamics associated with this additional, actuated spine degree of freedom. This model is sufficiently accurate as to roughly describe the robots mass center trajectory during a bounding limit cycle, thus making it a potential option for low dimensional representations of spine actuation in steady-state legged locomotion.

This work was supported in part by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0822 held by the first author and in part by ONR grant
#N00014-16-1-2817, a Vannevar Bush Fellowship sponsored
by the Basic Research Office of the Assistant Secretary of
Defense for Research and Engineering held by the second
author. We thank Benjamin Kramer and Benjamin Bernstein
for their support in maintaining and improving the Inu
platform.

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We present a model-based optimization framework that optimizes base pose and footholds simultaneously. It can generate motions in rough environments for a variety of different gaits in real time.

Title:
TAMOLS: Terrain-Aware Motion Optimization for Legged Systems

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

arXiv: arxiv.org/abs/2206.14049
code for mapping filters: github.com/leggedrobotics/elevation_mapping_cupy


Abstract:
Terrain geometry is, in general, non-smooth, non-linear, non-convex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This work presents the complete control pipeline capable of handling the aforementioned problems in real-time. We formulate a trajectory optimization problem that jointly optimizes over the base pose and footholds, subject to a heightmap. To avoid converging into undesirable local optima, we deploy a graduated optimization technique. We embed a compact, contact-force free stability criterion that is compatible with the non-flat ground formulation. Direct collocation is used as transcription method, resulting in a non-linear optimization problem that can be solved online in less than ten milliseconds. To increase robustness in the presence of external disturbances, we close the tracking loop with a momentum observer. Our experiments demonstrate stair climbing, walking on stepping stones, and over gaps, utilizing various dynamic gaits.

Acknowledgments:
This research was partially supported by the Swiss National Science Foundation (SNSF) as part of project No.188596, the European Union’s Horizon 2020 research and innovation programme under grant agreement No.780883 and No. 101016970, and the Swiss National Science Foundation through the National Centre of Competence in Research Robotics (NCCR Robotics).

Voice-over by Maria Alejandra Jaimes

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A team led by the University of California San Diego has developed a new system of algorithms that enables four-legged robots to walk and run on challenging terrain while avoiding both static and moving obstacles. The work brings researchers a step closer to building robots that can perform search and rescue missions or collect information in places that are too dangerous or difficult for humans.

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A trio of researchers at Korea Advanced Institute of Science and Technology, working with a colleague at the University of Illinois at Urbana-Champaign, has designed and built a working quadruped robot with magnetized feet that can climb on the walls and ceilings of metal buildings and structures. Read more at techxplore.com/news/2022-12-quadruped-robot-magnetized-feet-climb.html

In this video: Summary video explaining agile and versatile climbing with MARVEL.

Video Credit: Seungwoo Hong et al, Agile and versatile climbing on ferromagnetic surfaces with a quadrupedal robot, Science Robotics (2022). DOI: 10.1126/scirobotics.add1017

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Reinforcement learning (RL) has become a promising approach to developing controllers for quadrupedal robots. Conventionally, an RL design for locomotion follows a position-based paradigm, wherein an RL policy outputs target joint positions at a low frequency that are then tracked by a high-frequency proportional-derivative (PD) controller to produce joint torques. In contrast, for the model-based control of quadrupedal locomotion, there has been a paradigm shift from position-based control to torque-based control. In light of the recent advances in model-based control, we explore an alternative to the position-based RL paradigm, by introducing a torque-based RL framework, where an RL policy directly predicts joint torques at a high frequency, thus circumventing the use of a PD controller. The proposed learning torque control framework is validated with extensive experiments, in which a quadruped is capable of traversing various terrain and resisting external disturbances while following user-specified commands. Furthermore, compared to learning position control, learning torque control demonstrates the potential to achieve a higher reward and is more robust to significant external disturbances. To our knowledge, this is the first sim-to-real attempt for end-to-end learning torque control of quadrupedal locomotion.

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Website: https://agile-but-safe.github.io
Paper: https://arxiv.org/abs/2401.17583
Authors: Tairan He*, Chong Zhang*, Wenli Xiao, Guanqi He, Changliu Liu, Guanya Shi
LeCAR (Learning and Control for Agile Robotics) Lab, Carnegie Mellon University

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Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size. The learning process must usually be repeated for every new robot, where hyperparameters and reward function weights must be re-tuned to maximize performance for each new system. Alternatively, attempting to train a single policy to accommodate different robot sizes, while maintaining the same degrees of freedom (DoF) and morphology, requires either complex learning frameworks, or mass, inertia, and dimension randomization, which leads to
prolonged training periods. In our study, we show that drawing inspiration from animal motor control allows us to effectively train a single locomotion policy capable of controlling a diverse range of quadruped robots. The robot differences encompass: a variable number of DoFs, (i.e. 12 or 16 joints), three distinct morphologies, a broad mass range spanning from 2 kg to 200 kg, and nominal standing heights ranging from 18 cm to 100 cm. Our policy modulates a representation of the Central Pattern Generator (CPG) in the spinal cord, effectively coordinating both frequencies and amplitudes of the CPG to produce rhythmic output (Rhythm Generation), which is then mapped to a Pattern Formation (PF) layer. Across different robots, the only varying component is the PF layer, which adjusts the scaling parameters for the stride height and length. Subsequently, we evaluate the sim-to-real transfer by testing the single policy on both the Unitree Go1 and A1 robots. Remarkably, we observe robust performance, even when adding a 15 kg load, equivalent to 125% of the A1 robot’s nominal mass.

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