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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2023)

Creating machines that resemble a human morphology have always been an aspiring goal for mankind even from the early days of engineering. Today, this effort continues to exist especially because of how much adaptive and flexible humans can be, as we can carry out a wide variety of tasks by collectively using our arms and legs. We can run, fall, get back up, and even use our arms or tools to provide additional stability when carrying out different locomotion or manipulation tasks. Imagine how much more helpful machines could be in our lives if we could build human-like machines that could do even a fraction of what we can. Traditionally however, humanoids have been big, bulky machines that moved around very slowly and were susceptible to disturbances and falling down.

Recently, with the collective advancement of key technologies, we have seen a rapid growth in the number of quadrupeds that can now dynamically walk and run in outdoor environments. Quadrupeds are inherently a stable platform, which is opposite to a biped that is inherently unstable. Yet, the underlying core principles in both hardware and software could be selectively adopted in a humanoid context to enhance their agility and robustness.

Hence, this dissertation is an extension of such ideas on a humanoid to make our human-like machines dynamically walk and run such that they can do more meaningful tasks in the future. This requires a development of a hardware platform that uses similar design principles that were successful on quadrupeds, as well as a software and control stack that uses state-of-the-art techniques to robustly control the robot. Therefore, this dissertation introduces ARTEMIS, the first full-sized humanoid robot using proprioceptive actuators, its key design features, along with a real-time optimization-based dynamic locomotion stack that allows ARTEMIS to walk and run. Such hardware and software development allowed ARTEMIS to be the fastest walking humanoid reaching up to 2.1 m/s walking speed, be able to take aggressive pushes from all facets of its body, robustly walk without perception on debris cluttered terrain, and also be the very first humanoid fully developed in academia that can run. This opens up an exciting new chapter in the journey to developing humanoids that not only look like us, but can also robustly move and accomplish meaningful tasks.

  • 1 supplemental ZIP
Cover page: Development and Real-Time Optimization-based Control of a Full-sized Humanoid for Dynamic Walking and Running
  • Article
  • Peer Reviewed
UCLA Previously Published Works (2021)
This work presents the first full disclosure of BALLU, Buoyancy Assisted Lightweight Legged Unit, and describes the advantages and challenges of its concept, the hardware design of a new implementation (BALLU2), a motion analysis, and a data-driven walking controller. BALLU is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to the lightweight body, which solves many issues that hinder current robots from operating close to humans. The advantages gained also lead to the platforms distinct difficulties caused by severe nonlinearities and external forces such as buoyancy and drag. The paper describes the nonconventional characteristics of BALLU as a legged robot and then gives an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process using Spearman Correlation Coefficient is proposed to form low-dimensional state vectors from the simulation data, and an artificial neural network-based controller is trained on the same data. The controller is tested both on simulation and on real-world hardware. Its performance is assessed by observing the robots limit cycles and trajectories in the Cartesian coordinate. The controller generates periodic walking sequences in simulation as well as on the real-world robot even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLUs walking.
    Cover page: BALLU2: A Safe and Affordable Buoyancy Assisted Biped.Creative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2020)
    The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.
      Cover page: Clouds and Convective Self‐Aggregation in a Multimodel Ensemble of Radiative‐Convective Equilibrium Simulations
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