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Milad Siami

5 accepted papers

2026

BEYOND AMPLITUDE: CHANNEL STATE INFORMATION PHASE-AWARE DEEP FUSION FOR ROBOTIC ACTIVITY RECOGNITION

ICASSP 2026oral

Wi-Fi Channel State Information (CSI) has emerged as a promising non-line-of-sight sensing modality for human and robotic activity recognition. However, prior work has predominantly relied on CSI amplitude while underutilizing phase information, particularly in robotic arm activity recognition. In t…

Cited by 0SourcePDFScholar
2026

Non-Submodular Visual Attention for Robot Navigation

ICRA 2026poster

This paper presents a task-oriented computational framework to enhance Visual-Inertial Navigation (VIN) in robots, addressing challenges such as limited time and energy resources. The framework strategically selects visual features using a Mean Square Error (MSE)-based, non-submodular objective func…

2026

Real-Time Adaptive Motion Planning Via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields

ICRA 2026poster

Motivated by the problem of pursuit-evasion, we present a motion planning framework that combines energy-based diffusion models with artificial potential fields for robust real time trajectory generation in complex environments. Our approach processes obstacle information directly from point clouds,…

2025

Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields

RA-L 2025

Motivated by the problem of pursuit-evasion, we present a motion planning framework that combines energy-based diffusion models with artificial potential fields for robust real time trajectory generation in complex environments. Our approach processes obstacle information directly from point clouds,

Cited by 0SourceScholar
2024

Robustness Evaluation of Machine Learning Models for Robot Arm Action Recognition in Noisy Environments

ICASSP 2024accepted

In the realm of robot action recognition, identifying distinct but spatially proximate arm movements using vision systems in noisy environments poses a significant challenge. This paper studies robot arm action recognition in noisy environments using machine learning techniques. Specifically, a visi…

Cited by 0SourceScholar