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Found 20 papers, 3 papers with code
PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields
Through extensive experiments, we demonstrate the effectiveness of PlanarNeRF in various scenarios and remarkable improvement over existing works.
GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments
This study presents the GP-MPPI, an online learning-based control strategy that integrates MPPI with a local perception model based on Sparse Gaussian Process (SGP).
Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction
We prove that our CALI model -- a pseudo-trilateral game structure is advantageous over existing bilateral game structures.
IDA: Informed Domain Adaptive Semantic Segmentation
To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup.
SePaint: Semantic Map Inpainting via Multinomial Diffusion
Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution.
Decision-Making Among Bounded Rational Agents
Using both simulated and real-world experiments in multi-robot navigation tasks, we demonstrate that the resulting framework allows the robots to reason about different levels of rational behaviors of other agents and compute a reasonable strategy under its computational constraint.
Artificial Neural Network-Based Voltage Control of DC/DC Converter for DC Microgrid Applications
Due to the advantages of the DC distribution system such as easy integration of energy storage and less system loss, DC MG attracts significant attention nowadays.
Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation
Detecting navigable space is a fundamental capability for mobile robots navigating in unknown or unmapped environments.
An Artificial Neural Network-Based Model Predictive Control for Three-phase Flying Capacitor Multi-Level Inverter
The case study herein is based on a four-level three-cell flying capacitor inverter.
Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles
We first predict the future state distributions of other vehicles to account for their uncertain behaviors affected by the time-varying disturbances.
Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes
We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs).
Reachable Space Characterization of Markov Decision Processes with Time Variability
We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments.
Creating Navigable Space from Sparse Noisy Map Points
We present a framework for creating navigable space from sparse and noisy map points generated by sparse visual SLAM methods.
Robotics
AutoQ: Automated Kernel-Wise Neural Network Quantization
Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy.
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times
A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper.
Accelerating Goal-Directed Reinforcement Learning by Model Characterization
Then, we leverage this approximate model along with a notion of reachability using Mean First Passage Times to perform Model-Based reinforcement learning.
Reachability and Differential based Heuristics for Solving Markov Decision Processes
The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states.
A Spatio-Temporal Representation for the Orienteering Problem with Time-Varying Profits
Different from most existing works where the profits are assumed to be static, in this work we investigate a variant that has arbitrary time-dependent profits.
Informative Planning and Online Learning with Sparse Gaussian Processes
A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed.
A Solution to Time-Varying Markov Decision Processes
We consider a decision-making problem where the environment varies both in space and time.
