SO(2)-Equivariant Downwash Models for Close Proximity Flight

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Smith, H., Shankar, A., Gielis, J., Blumenkamp, J., Prorok, A.
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Robotics Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Language-Conditioned Offline RL for Multi-Robot Navigation
di: Morad, Steven, et al.
Pubblicazione: (2024)

System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning
di: Bettini, Matteo, et al.
Pubblicazione: (2023)

CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot Applications
di: Blumenkamp, Jan, et al.
Pubblicazione: (2024)

Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning
di: Bettini, Matteo, et al.
Pubblicazione: (2024)

Graph Attention-Guided Search for Dense Multi-Agent Pathfinding
di: Jain, Rishabh, et al.
Pubblicazione: (2025)

Equivariant Action Sampling for Reinforcement Learning and Planning
di: Zhao, Linfeng, et al.
Pubblicazione: (2024)

A Data-Based Architecture for Flight Test without Test Points
di: Harp, D. Isaiah, et al.
Pubblicazione: (2025)

Causal Composition Diffusion Model for Closed-loop Traffic Generation
di: Lin, Haohong, et al.
Pubblicazione: (2024)

Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains
di: Nobrega, Lucas Nogueira, et al.
Pubblicazione: (2024)

PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL
di: McClellan, Joshua, et al.
Pubblicazione: (2025)

Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control
di: Yamada, Jun, et al.
Pubblicazione: (2025)

Autonomous UAV Flight Navigation in Confined Spaces: A Reinforcement Learning Approach
di: Tayar, Marco S., et al.
Pubblicazione: (2025)

Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking
di: Chang, Kevin, et al.
Pubblicazione: (2025)

Continual Driving Policy Optimization with Closed-Loop Individualized Curricula
di: Niu, Haoyi, et al.
Pubblicazione: (2023)

Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
di: O'Connell, Michael, et al.
Pubblicazione: (2022)

SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation
di: Stoler, Benjamin, et al.
Pubblicazione: (2024)

Wake Up to the Past: Using Memory to Model Fluid Wake Effects on Robots
di: Vendruscolo, Luca, et al.
Pubblicazione: (2026)

Categorical Equivariant Deep Learning: Category-Equivariant Neural Networks and Universal Approximation Theorems
di: Maruyama, Yoshihiro
Pubblicazione: (2025)

Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
di: Hu, Mengkang, et al.
Pubblicazione: (2023)

Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization
di: Kapoor, Aditya, et al.
Pubblicazione: (2024)

cc-DRL: a Convex Combined Deep Reinforcement Learning Flight Control Design for a Morphing Quadrotor
di: Yang, Tao, et al.
Pubblicazione: (2024)

DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion
di: Kalaria, Dvij, et al.
Pubblicazione: (2025)

Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
di: Zheng, Ruijie, et al.
Pubblicazione: (2024)

Distilling On-device Language Models for Robot Planning with Minimal Human Intervention
di: Ravichandran, Zachary, et al.
Pubblicazione: (2025)

Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection
di: Paudel, Abhishek, et al.
Pubblicazione: (2026)

Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
di: Stölzle, Maximilian, et al.
Pubblicazione: (2024)

An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis
di: Galbiati, Federico, et al.
Pubblicazione: (2024)

Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
di: Kruse, Liam A., et al.
Pubblicazione: (2025)

ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
di: Amir, Michael, et al.
Pubblicazione: (2025)

Align2Act: Instruction-Tuned Models for Human-Aligned Autonomous Driving
di: Jaisankar, Kanishkha, et al.
Pubblicazione: (2025)

R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation
di: Morihira, Naoki, et al.
Pubblicazione: (2026)

V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models
di: You, Junwei, et al.
Pubblicazione: (2024)

Prompting Robot Teams with Natural Language
di: Pfitzer, Nicolas, et al.
Pubblicazione: (2025)

Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour
di: Harrison, Emma M. A.
Pubblicazione: (2025)

A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
di: Abouelazm, Ahmed, et al.
Pubblicazione: (2024)

Failure Probability Estimation for Black-Box Autonomous Systems using State-Dependent Importance Sampling Proposals
di: Delecki, Harrison, et al.
Pubblicazione: (2024)

Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model
di: Yuan, Xiu, et al.
Pubblicazione: (2024)

SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling
di: Zhang, Jesse, et al.
Pubblicazione: (2023)

Semantic World Models
di: Berg, Jacob, et al.
Pubblicazione: (2025)

Demonstration-Free Robotic Control via LLM Agents
di: Tsui, Brian Y., et al.
Pubblicazione: (2026)