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Found 8 papers, 0 papers with code
Biomechanic Posture Stabilisation via Iterative Training of Multi-policy Deep Reinforcement Learning Agents
In this work we address the error propagation problem by introducing an iterative training procedure for deep reinforcement learning which allows the agent to learn a finite set of actions and how to coordinate between them in order to achieve a stable standing posture.
An ocular biomechanics environment for reinforcement learning
Reinforcement learning has been applied to human movement through physiologically-based biomechanical models to add insights into the neural control of these movements; it is also useful in the design of prosthetics and robotics.
Fast Synthetic LiDAR Rendering via Spherical UV Unwrapping of Equirectangular Z-Buffer Images
In this paper, we present a novel method to simulate LiDAR point cloud with faster rendering time of 1 sec per frame.
Refined Continuous Control of DDPG Actors via Parametrised Activation
In this paper, we propose enhancing actor-critic reinforcement learning agents by parameterising the final actor layer which produces the actions in order to accommodate the behaviour discrepancy of different actuators, under different load conditions during interaction with the environment.
Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data
We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors.
Ranked #2 on
Unsupervised Domain Adaptation
on PreSIL to KITTI
Real-time Intent Prediction of Pedestrians for Autonomous Ground Vehicles via Spatio-Temporal DenseNet
We trained and evaluated our framework based on real data collected from urban traffic environments.
Realistic Hair Simulation Using Image Blending
In this presented work, we propose a realistic hair simulator using image blending for dermoscopic images.
SSIMLayer: Towards Robust Deep Representation Learning via Nonlinear Structural Similarity
Deeper convolutional neural networks provide more capacity to approximate complex mapping functions.
