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New Bi-Mamba Model Enhances Robot Navigation with Real-Time Trajectory Prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TrajMamba, a novel bidirectional state space model designed for real-time human trajectory prediction using RGB-D cameras. This model addresses the train-deploy pipeline gap by aligning pose extraction and depth processing for both training and inference. TrajMamba utilizes stacked bidirectional Mamba blocks to encode velocity data and a GRU head to decode future trajectories, achieving an average ADE of 0.29m and FDE of 0.51m on the ETH/UCY benchmark. With a compact 262K parameters, it operates efficiently on hardware like the NVIDIA Jetson Orin, making it suitable for deployment on mobile robots. AI

IMPACT This model's efficiency and real-time performance could accelerate the deployment of advanced navigation capabilities in mobile robotics.

RANK_REASON Academic paper detailing a new model architecture and its performance on a benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Bi-Mamba Model Enhances Robot Navigation with Real-Time Trajectory Prediction

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Academic paper detailing a new model architecture and its performance on a benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Basheer Al-Tawil, Magnus Jung, Ayoub Al-Hamadi ·

    Real-Time Human Trajectory Prediction from RGB-D for Mobile Robots Using Bi-Mamba

    arXiv:2610.33916v1 Announce Type: cross Abstract: Predicting the short-term future trajectory of a person from RGB-D cameras is a key capability for mobile robots navigating in dynamic and human populated environment. The key challenge to deploying learned predictors on real robo…

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