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Found 17 papers, 3 papers with code
Machine learning based state observer for discrete time systems evolving on Lie groups
Conventional techniques involving machine learning based observers on systems evolving on Lie groups involve designing charts for the Lie group, training a machine learning based observer for each chart, and switching between the trained models based on the state of the system.
Transversally exponentially stable Euclidean space extension technique for discrete time systems
We propose a modification technique for discrete time systems for exponentially fast convergence to compact sets.
Globally exponentially convergent observer for systems evolving on matrix Lie groups
We propose a globally exponentially convergent observer for the dynamical system evolving on matrix Lie groups with bounded velocity with unknown bound.
Angular velocity and linear acceleration measurement bias estimators for the rigid body system with global exponential convergence
Rigid body systems usually consider measurements of the pose of the body using onboard cameras/LiDAR systems, that of linear acceleration using an accelerometer and of angular velocity using an IMU.
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs
In particular, inspired by a numerical integration method on manifolds called Feedback Integrators, we propose to instantiate it on the tangent bundle of the Stiefel manifold for the first time.
Robust Navigation for Racing Drones based on Imitation Learning and Modularization
This paper presents a vision-based modularized drone racing navigation system that uses a customized convolutional neural network (CNN) for the perception module to produce high-level navigation commands and then leverages a state-of-the-art planner and controller to generate low-level control commands, thus exploiting the advantages of both data-based and model-based approaches.
The Adaptive Dynamic Programming Toolbox
The paper develops the Adaptive Dynamic Programming Toolbox (ADPT), which solves optimal control problems for continuous-time nonlinear systems.
Double Prioritized State Recycled Experience Replay
Experience replay enables online reinforcement learning agents to store and reuse the previous experiences of interacting with the environment.
Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving
We present a training pipeline for the autonomous driving task given the current camera image and vehicle speed as the input to produce the throttle, brake, and steering control output.
Deep Reinforcement Learning Based Robot Arm Manipulation with Efficient Training Data through Simulation
Deep reinforcement learning trains neural networks using experiences sampled from the replay buffer, which is commonly updated at each time step.
Robotics Systems and Control Systems and Control
A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning
In this paper, we propose a dual memory structure for reinforcement learning algorithms with replay memory.
Learning-Driven Exploration for Reinforcement Learning
We introduce entropy-based exploration (EBE) that enables an agent to explore efficiently the unexplored regions of state space.
Enhancement of Energy-Based Swing-Up Controller via Entropy Search
An energy based approach for stabilizing a mechanical system has offered a simple yet powerful control scheme.
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)
Robotics I.2.9; I.2.0
Towards Robust Neural Networks with Lipschitz Continuity
Deep neural networks have shown remarkable performance across a wide range of vision-based tasks, particularly due to the availability of large-scale datasets for training and better architectures.
A Novel Representation of Neural Networks
However, the representation is non-standardized, and the gradient calculation methods are often performed using component-based approaches that break parameters down into scalar units, instead of considering the parameters as whole entities.
A Geometric Framework for Convolutional Neural Networks
In this paper, a geometric framework for neural networks is proposed.
