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Found 7 papers, 1 papers with code
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning
Despite progress in differential privacy and generative modeling for privacy-preserving data release in the literature, only a few approaches optimize for machine learning utility: most approaches only take into account statistical metrics on the data itself and fail to explicitly preserve the loss metrics of machine learning models that are to be subsequently trained on the generated data.
Training Humans to Train Robots Dynamic Motor Skills
Learning from demonstration (LfD) is commonly considered to be a natural and intuitive way to allow novice users to teach motor skills to robots.
Exploiting Ergonomic Priors in Human-to-Robot Task Transfer
The main advantage to using this is generalisation of a task by retargeting a systems redundancy as well as the capability to fully replace an entire system with another of varying link number and lengths while still accurately repeating a task subject to the same constraints.
Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation
Learning from Demonstration depends on a robot learner generalising its learned model to unseen conditions, as it is not feasible for a person to provide a demonstration set that accounts for all possible variations in non-trivial tasks.
A Library for Constraint Consistent Learning
This paper introduces the first, open source software library for Constraint Consistent Learning (CCL).
Learning Singularity Avoidance
With the increase in complexity of robotic systems and the rise in non-expert users, it can be assumed that task constraints are not explicitly known.
Learning Null Space Projections in Operational Space Formulation
In this paper, we consider learning the null space projection matrix of a kinematically constrained system in the absence of any prior knowledge either on the underlying policy, the geometry, or dimensionality of the constraints.
