#simulation
21 papers match
Learning Social Robot Navigation By Sensing Human Legs
Alberto Vaglio, Andrea Garulli, Antonio Giannitrapani +2
The paper introduces CALF, an end‑to‑end neural network that reads 2‑D LiDAR scans of human legs and outputs socially compliant navigation commands for robots, trained via deep rei…
VAmoS Bench: Voice Agent Simulation Bench
Joshua Meyer, Sahar Shayegan, Ritiz Tambi +5
The paper presents VAmoS Bench, a simulation-based benchmark that evaluates complete voice‑agent systems on end‑to‑end customer‑support tasks, checking both conversational behavior…
SONG: A Photorealistic 3D Gaussian Simulation Platform for Benchmarking Social Navigation
Weiqi Huang, Dianyi Yang, Jiaxin Li +4
The paper presents SONG, a photorealistic 3D simulation platform that uses 3D Gaussian splatting to generate realistic visual scenes and moving human avatars for evaluating vision‑…
Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
Ya-Chia Shen, Woei-Leong Chan
The paper presents an end-to-end deep reinforcement learning framework that directly controls quadcopter thrust and body torques while incorporating realistic actuator dynamics and…
Spline-Based Boundary Representations for Sparse View Reconstruction and Simulation Using Isogeometric Analysis
Davor Dobrota, Vsevolod Skorokhodov, Chenghao Xu +2
The paper introduces FORGE-SIM, a method that reconstructs smooth, watertight multi‑patch B‑spline models directly from sparse RGB images and projects observation data onto them fo…
Toward a Characterization of Simulation Between Arithmetic Theories
Hunter Monroe
The paper investigates when a sound arithmetic theory can efficiently prove bounded consistency statements of its own extensions, providing constraints on such simulations and prop…
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
Byeongguk Jeon, Seonghyeon Ye, JaeHyeok Doo +4
RoboWorld is an automated pipeline that uses a fast autoregressive video world model and a vision-language scoring system to evaluate generalist robot policies efficiently and reli…
Automatic Testing of Interacting Autonomous Vehicles
Fabio Cavaleri, Alessio Gambi, Paolo Arcaini +2
The paper introduces EVITA, a method that uses multi‑objective optimization to automatically generate diverse simulation scenarios that involve multiple interacting autonomous vehi…
Enhanced wavefront sensing for the Roman Coronagraph Instrument: Gaussian probes and compact model validation
Lukas Delaye, Iva Laginja, Pierre Baudoz +10
The paper evaluates the use of Gaussian probe patterns for focal‑plane wavefront sensing in the Roman Space Telescope's Coronagraph Instrument, showing via simulations that they ca…
JoyAI-Sim: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid
Peidong Liu, Yongce Liu, Songyan Guo +34
JoyAI-Sim is a toolchain that connects real robots, simulation, and human demonstrations to enable scalable evaluation and generation of robot training data using calibrated digita…
Revealed Attentional Interference
Paul H. Y. Cheung, Yi-Hsuan Lin, Chung-Hao Sheu
The paper analyzes how external stimuli affect attention by modeling proactive and retroactive interference in attention formation, deriving bounds on parameters that govern inform…
TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
Zikang Xiong, Weixin Li, Zhouchonghao Wu +6
The paper proposes a method to train end-to-end autonomous driving policies without expert demonstrations by pretraining a policy via self‑play in a fast vectorized simulator and t…
An offline approach to fNIRS-guided reinforcement learning for robot behavior
Julia Santaniello, Madelaine Brower, Benson Jiang +3
The paper investigates using offline functional near‑infrared spectroscopy (fNIRS) brain signals to augment reinforcement learning for robot behavior, showing that neural data can…
EvalNet: A Practical Toolchain for Generation and Analysis of Extreme-Scale Interconnects
Maciej Besta, Patrick Iff, Marcel Schneider +10
EvalNet is a practical toolchain that generates and analyzes a wide range of extreme‑scale network topologies, providing detailed metrics on shortest and non‑shortest path diversit…
The Seriality Gap in Video Diffusion Models
Jorge Diaz Chao, Konpat Preechakul, Yuxi Liu +1
The paper investigates why video diffusion models struggle with tasks that require sequential causal reasoning, such as multi‑ball collisions, and identifies a "seriality gap" wher…
Microflow: Microarchitectural Causal Observability for Deep Cross-Layer Analysis and Optimization
Saber Ganjisaffar, Chengyu Song, Nael Abu-Ghazaleh
Microflow is a framework that converts execution traces into a causal intermediate representation, allowing precise attribution of microarchitectural stalls to their root causes ac…
Algorithms for generating planar networks simulating hierarchical patterns of cracks formed during film drying
Yuri Yu. Tarasevich, Andrei V. Eserkepov, Andrei S. Burmistrov
The paper studies the geometry and topology of hierarchical crack patterns formed in drying thin films, using image analysis and graph theory, and proposes three algorithms to gene…
Tracing boron diffusion into a textured silicon solar cell using electron beam induced current in a scanning transmission electron microscope
Tobias Meyer, David A. Ehrlich, Peter Pichler +7
The paper combines scanning transmission electron beam induced current measurements with simulations to map boron dopant distribution beneath the pyramid‑textured surface of silico…
Constrained Reinforcement Learning for Safe Heat Pump Control
Baohe Zhang, Lilli Frison, Thomas Brox +1
The paper introduces a building simulator (I4B) and a constrained Soft Actor-Critic algorithm with a linear smoothed log barrier (CSAC-LB) to safely optimize heat pump operation, b…
RetroHolmes: When Semantic Plausibility Fails Retrospective Physical Process Reasoning
Ruoxuan Zhang, Qiyun Zheng, Siyu Wu +12
The paper presents RetroHolmes, a benchmark for testing vision‑language models on retrospective physical process reasoning—inferring hidden causes from image outcomes—and shows tha…
Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty
Jeroen F. Uleman, Loes Crielaard, Leonie K. Elsenburg +4
The paper introduces Diagrams-to-Dynamics (D2D), a method that transforms qualitative causal loop diagrams into exploratory system dynamics models without requiring empirical data,…
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