Search Results for author:
Found 22 papers, 8 papers with code
Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception
Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer.
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.
An autonomous swarm of micro flying robots with range-based relative localization
Accurate relative localization is an important requirement for a swarm of robots, especially when performing a cooperative task.
Robotics Multiagent Systems
MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models
MAMBPO uses a learned world model to improve sample efficiency compared to model-free Multi-Agent Soft Actor-Critic (MASAC).
CNN-based Ego-Motion Estimation for Fast MAV Maneuvers
In the field of visual ego-motion estimation for Micro Air Vehicles (MAVs), fast maneuvers stay challenging mainly because of the big visual disparity and motion blur.
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning
Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones.
CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry
Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world.
Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow
In addition, a method for estimating the divergence from event-based optical flow is introduced, which accounts for the aperture problem.
Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning
We then formally show that these local states can only coexist when the global desired pattern is achieved and that, until this occurs, there is always a sequence of actions that will lead from the current pattern to the desired pattern.
Robotics
How do neural networks see depth in single images?
We further show that MonoDepth's use of the vertical image position allows it to estimate the distance towards arbitrary obstacles, even those not appearing in the training set, but that it requires a strong edge at the ground contact point of the object to do so.
Evolution of Robust High Speed Optical-Flow-Based Landing for Autonomous MAVs
Automatic optimization of robotic behavior has been the long-standing goal of Evolutionary Robotics.
A model-based framework for learning transparent swarm behaviors
The framework is based on the automatic extraction of two distinct models: 1) a neural network model trained to estimate the relationship between the robots' sensor readings and the global performance of the swarm, and 2) a probabilistic state transition model that explicitly models the local state transitions (i. e., transitions in observations from the perspective of a single robot in the swarm) given a policy.
EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following
Our network can detect propellers at a rate of 85. 1% even when 60% of the propeller is occluded and can run at upto 35Hz on a 2W power budget.
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy
In this work we approach, for the first time, the intensity reconstruction problem from a self-supervised learning perspective.
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
Machine learning (ML) has become a pervasive tool across computing systems.
NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter
In this work, we present NanoFlowNet, a lightweight convolutional neural network for real-time dense optical flow estimation on edge computing hardware.
Rethinking Event-based Optical Flow: Iterative Deblurring as an Alternative to Correlation Volumes
Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and store, at the same time prohibiting them from estimating high-resolution flow.
Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow
Event cameras have recently gained significant traction since they open up new avenues for low-latency and low-power solutions to complex computer vision problems.
Neuromorphic Control using Input-Weighted Threshold Adaptation
In addition, integration is an important part of any temporal task, so the proposed Input-Weighted Threshold Adaptation (IWTA) mechanism may have implications well beyond control tasks.
Optimality Principles in Spacecraft Neural Guidance and Control
Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission.
Unified incremental nonlinear controller for the transition control of a hybrid dual-axis tilting rotor quad-plane
Overactuated Tilt Rotor Unmanned Aerial Vehicles are renowned for exceptional wind resistance and a broad operational range, which poses complex control challenges due to non-affine dynamics.
TDE-3: An improved prior for optical flow computation in spiking neural networks
Using synthetic data we compared training and inference with spike count and ISI with respect to changes in stimuli dynamic range, spatial frequency, and level of noise.
