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Found 14 papers, 9 papers with code
A Benchmark for Cycling Close Pass Near Miss Event Detection from Video Streams
Instance-level detection aims to detect which vehicle in the scene gives rise to a close pass near miss.
Partial Observability during DRL for Robot Control
Deep Reinforcement Learning (DRL) has made tremendous advances in both simulated and real-world robot control tasks in recent years.
Natural Language Communication with a Teachable Agent
The results indicate that teaching via paraphrasing and text input has a positive effect on learning outcomes for the material covered, and also on aspects of affective engagement.
Memory-based Deep Reinforcement Learning for POMDPs
A promising characteristic of Deep Reinforcement Learning (DRL) is its capability to learn optimal policy in an end-to-end manner without relying on feature engineering.
Fast Approximate Multi-output Gaussian Processes
Training with the proposed approach requires computing only a $N \times n$ eigenfunction matrix and a $n \times n$ inverse where $n$ is a selected number of eigenvalues.
Learning from Sparse Demonstrations
The time stamps of the keyframes can be different from the time of the robot's actual execution.
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning
Recently, research in Deep Reinforcement Learning (DRL) also shows that multi-step methods improve learning speed and final performance in applications where the value-function and policy are represented with deep neural networks.
Supportive Actions for Manipulation in Human-Robot Coworker Teams
We compare two robot modes in a shared table pick-and-place task: (1) Task-oriented: the robot only takes actions to further its own task objective and (2) Supportive: the robot sometimes prefers supportive actions to task-oriented ones when they reduce future goal-conflicts.
A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease
The assessment of Parkinson's disease (PD) poses a significant challenge as it is influenced by various factors which lead to a complex and fluctuating symptom manifestation.
Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization
Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements.
Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems
The model fidelity is used to adapt the feedback gains allowing low feedback gains in state space regions of high model confidence.
Inverse Optimal Control with Incomplete Observations
We handle the problem by proposing the recovery matrix, which establishes a relationship between available observations of the trajectory and weights of given candidate features.
Robotics Systems and Control
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training.
Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks
The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems.
