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Found 6 papers, 1 papers with code
simPLE: a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
We propose simPLE (simulation to Pick Localize and PLacE) as a solution to precise pick-and-place.
Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials
Adopting shared data resources requires scientists to place trust in the originators of the data.
A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions
Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows.
Quantifying Transparency of Machine Learning Systems through Analysis of Contributions
Increased adoption and deployment of machine learning (ML) models into business, healthcare and other organisational processes, will result in a growing disconnect between the engineers and researchers who developed the models and the model's users and other stakeholders, such as regulators or auditors.
Defining the Collective Intelligence Supply Chain
Organisations are increasingly open to scrutiny, and need to be able to prove that they operate in a fair and ethical way.
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Since product images are readily available for a wide range of objects (e. g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data.
