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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2016)

Several areas of computer science, including computer vision and natural

language processing, have witnessed rapid advances in performance, due in part

to shared datasets and benchmarks. In a robotics setting, benchmarking is

challenging due to the amount of variation in common applications: researchers can use

different robots, different objects, different algorithms, different tasks, and

different environments.

Cloud robotics, in which a robot accesses computation and data over a network,

may help address the challenge of benchmarking in robotics. By standardizing

the interfaces in which robotic systems access and store data, we can define a

common set of tasks and compare the performance of various systems.

In this dissertation, we examine two problem settings that are well served by

cloud robotics. We also discuss two datasets that facilitate benchmarking of

several problems in robotics. Finally, we discuss a framework for defining and

using cloud-based robotic services.

The first problem setting is object instance recognition. We present an

instance recognition system which uses a library of high-fidelity object models

of textured household objects. The system can handle occlusions, illumination

changes, multiple objects, and multiple instances of the same object.

The next problem setting is clothing recognition and manipulation. We propose a

method that enables a general purpose robot to bring clothing articles into a

desired configuration from an unknown initial configuration. Our method uses a

library of simple clothing models and requires limited perceptual capabilities.

Next, we present BigBIRD (Big Berkeley Instance Recognition Dataset), which has

been used in several areas relevant to cloud robotics, including instance

recognition, grasping and manipulation, and 3D model reconstruction. BigBIRD

provides 600 3D point clouds and 600 high-resolution (12 MP) images covering

all views of each object, along with generated meshes for ease of use. We also

explain the details of our calibration procedure and data collection system,

which collects all required data for a single object in under five minutes with

minimal human effort.

We then discuss the Yale-CMU-Berkeley (YCB) Object and Model Set, which is

specifically designed for benchmarking in manipulation research. For a set of

everyday objects, the dataset provides the same data as BigBIRD, an additional

set of high-quality models, and formats for use with common robotics software

packages. Researchers can also obtain a physical set of the objects, enabling

both simulation-based and robotic experiments.

Lastly, we discuss Brass, a preliminary framework for providing robotics and

automation algorithms as easy-to-use cloud services. Brass can yield several

benefits to algorithm developers and end-users, including automatic

resource provisioning and load balancing, benchmarking, and collective robot

learning.

    Cover page: Benchmarks for Cloud Robotics
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2011)
    • 2 supplemental videos
    Cover page: Bringing Clothing into Desired Configurations with Limited Perception
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2011)
    We consider the problem of autonomously bringing an article of clothing into a desired configuration using a general-purpose two-armed robot. We propose a hidden Markov model (HMM) for estimating the identity of the article and tracking the article's configuration throughout a specific sequence of manipulations and observations. At the end of this sequence, the article's configuration is known, though not necessarily desired. The estimated identity and configuration of the article are then used to plan a second sequence of manipulations that brings the article into the desired configuration. We propose a relaxation of a strain-limiting finite element model for cloth simulation that can be solved via convex optimization; this serves as the basis of the transition and observation models of the HMM. The observation model uses simple perceptual cues consisting of the height of the article when held by a single gripper and the silhouette of the article when held by two grippers. The model accurately estimates the identity and configuration of clothing articles, enabling our procedure to autonomously bring a variety of articles into desired configurations that are useful for other tasks, such as folding. © 2011 IEEE.
    • 2 supplemental videos
    Cover page: Bringing Clothing into Desired Configurations with Limited Perception
    • Article
    • Peer Reviewed
    UC Davis Previously Published Works (2023)
    Receptor clustering is the most critical step to activate extrinsic apoptosis by death receptors belonging to the TNF superfamily. Although clinically unsuccessful, using agonist antibodies, the death receptors-5 remains extensively studied from a cancer therapeutics perspective. However, despite its regulatory role and elevated function in ovarian and other solid tumors, another tumor-enriched death receptor called Fas (CD95) remained undervalued in cancer immunotherapy until recently, when its role in off-target tumor killing by CAR-T therapies was imperative. By comprehensively analyzing structure studies in the context of the binding epitope of FasL and various preclinical Fas agonist antibodies, we characterize a highly significant patch of positively charged residue epitope (PPCR) in its cysteine-rich domain 2 of Fas. PPCR engagement is indispensable for superior Fas agonist signaling and CAR-T bystander function in ovarian tumor models. A single-point mutation in FasL or Fas that interferes with the PPCR engagement inhibited apoptotic signaling in tumor cells and T cells. Furthermore, considering that clinical and immunological features of the autoimmune lymphoproliferative syndrome (ALPS) are directly attributed to homozygous mutations in FasL, we reveal differential mechanistic details of FasL/Fas clustering at the PPCR interface compared to described ALPS mutations. As Fas-mediated bystander killing remains vital to the success of CAR-T therapies in tumors, our findings highlight the therapeutic analytical design for potentially effective Fas-targeting strategies using death agonism to improve cancer immunotherapy in ovarian and other solid tumors.
      Cover page: Characterizing the regulatory Fas (CD95) epitope critical for agonist antibody targeting and CAR-T bystander function in ovarian cancer.Creative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2025)
      Triple-negative breast cancer (TNBC) disproportionately affects younger Black women, who show more aggressive phenotypes and poorer outcomes than women of other racial identities. While the impact of socioenvironmental inequities within and beyond health systems is well documented, the genetic influence in TNBC-associated racial disparities remains elusive. Here, we report that cancer-free breast tissue from Black women expresses TRIM37 at a significantly higher level relative to White women. A reporter-based screen for regulatory variants identifies a non-coding risk variant rs57141087 in the 5' gene upstream region of the TRIM37 locus with enhancer activity. Mechanistically, rs57141087 increases enhancer-promoter interactions through NRF1, resulting in stronger TRIM37 promoter activity. Phenotypically, high TRIM37 levels drive neoplastic transformations in immortalized breast epithelial cells. Finally, context-dependent TRIM37 expression reveals that early-stage TRIM37 levels affect the initiation and trajectory of breast cancer progression. Together, our results indicate a genotype-informed association of oncogenic TRIM37 with TNBC risk in Black women and implicate TRIM37 as a predictive biomarker to better identify patients at risk of aggressive TNBC.
        Cover page: The TRIM37 variant rs57141087 contributes to triple-negative breast cancer outcomes in Black women
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