MultiUAV-Plat supplies a new RESTful simulation platform and 1500-task benchmark where Agent4Drone reaches 57.9% task pass rate versus 30.6% for ReAct baseline across 75 multi-UAV missions.
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
abstract
Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.
citation-role summary
background 1
baseline 1
citation-polarity summary
verdicts
UNVERDICTED 10representative citing papers
Large multimodal models display emerging but limited spatial action capabilities in goal-oriented urban 3D navigation, remaining far from human-level performance with errors diverging rapidly after critical decision points.
FWAV-Sim is a high-fidelity Unity simulation framework for flapping-wing vehicles that integrates blade-element aerodynamics with bluff-body drag, spatiotemporally correlated fractal turbulence, and realistic IMU/LiDAR/RGB sensor models to support autonomy development.
Isaac Lab is a unified GPU-native platform combining high-fidelity physics, photorealistic rendering, multi-frequency sensors, domain randomization, and learning pipelines for scalable multi-modal robot policy training.
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
Compute and motion are tightly intertwined in MAVs, requiring cyber-physical co-design for optimal mission metrics, as shown via analytical models, simulation, end-to-end benchmarking, and the open-sourced MAVBench tool suite.
A malicious agent in multi-agent LLM consensus systems can be trained via a surrogate world model and RL to reduce consensus rates and prolong disagreement more effectively than direct prompt attacks.
ABot-Earth 0.5 is a 3DGS-based generative model trained on real-world urban reconstructions that synthesizes novel 3D scenes from satellite imagery in under 10 minutes per square kilometer.
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.
ORRB is an open-source remote rendering backend that pairs Unity3d with MuJoCo for high-throughput, customizable visual domain randomization in robotics environments.
citing papers explorer
-
MultiUAV-Plat: An LLM-Oriented Platform, Benchmark and Framework for Multi-UAV Collaborative Task Planning
MultiUAV-Plat supplies a new RESTful simulation platform and 1500-task benchmark where Agent4Drone reaches 57.9% task pass rate versus 30.6% for ReAct baseline across 75 multi-UAV missions.
-
How Far Are Large Multimodal Models from Human-Level Spatial Action? A Benchmark for Goal-Oriented Embodied Navigation in Urban Airspace
Large multimodal models display emerging but limited spatial action capabilities in goal-oriented urban 3D navigation, remaining far from human-level performance with errors diverging rapidly after critical decision points.
-
A Simulation Platform for Flapping-Wing Vehicles
FWAV-Sim is a high-fidelity Unity simulation framework for flapping-wing vehicles that integrates blade-element aerodynamics with bluff-body drag, spatiotemporally correlated fractal turbulence, and realistic IMU/LiDAR/RGB sensor models to support autonomy development.
-
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
Isaac Lab is a unified GPU-native platform combining high-fidelity physics, photorealistic rendering, multi-frequency sensors, domain randomization, and learning pipelines for scalable multi-modal robot policy training.
-
ERPPO: Entropy Regularization-based Proximal Policy Optimization
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
-
The Role of Compute in Autonomous Aerial Vehicles
Compute and motion are tightly intertwined in MAVs, requiring cyber-physical co-design for optimal mission metrics, as shown via analytical models, simulation, end-to-end benchmarking, and the open-sourced MAVBench tool suite.
-
Insider Attacks in Multi-Agent LLM Consensus Systems
A malicious agent in multi-agent LLM consensus systems can be trained via a surrogate world model and RL to reduce consensus rates and prolong disagreement more effectively than direct prompt attacks.
-
ABot-Earth 0.5: Generative 3D Earth Model
ABot-Earth 0.5 is a 3DGS-based generative model trained on real-world urban reconstructions that synthesizes novel 3D scenes from satellite imagery in under 10 minutes per square kilometer.
-
Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.
-
ORRB -- OpenAI Remote Rendering Backend
ORRB is an open-source remote rendering backend that pairs Unity3d with MuJoCo for high-throughput, customizable visual domain randomization in robotics environments.