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Found 7 papers, 2 papers with code
Time Series Classification for Detecting Parkinson's Disease from Wrist Motions
We used a random search to find the highest-scoring InceptionTime architecture and compared it to ROCKET with a ridge classifier and a multi-layer perceptron (MLP) on wrist motions of PD patients.
Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions
Ensuring safety is of paramount importance in physical human-robot interaction applications.
Multi-Modal Learning of Keypoint Predictive Models for Visual Object Manipulation
Our evaluation shows that our approach learns to consistently predict visual keypoints on objects in the manipulator's hand, and thus can easily facilitate learning an extended kinematic chain to include the object grasped in various configurations, from a few seconds of visual data.
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem.
Deep Learning based Uncertainty Decomposition for Real-time Control
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration.
Learning State-Dependent Losses for Inverse Dynamics Learning
In both settings, the structured and state-dependent learned losses improve online adaptation speed, when compared to standard, state-independent loss functions.
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations
Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision making.
