Track-MM: Dual-Memory Embodied Visual Tracking with Latent Motion Priors

Embodied visual tracking (EVT) is an essential capability for embodied agents, which needs persistent target tracking in dynamic, partially observable environments. The core challenge lies in maintaining target identity under distraction with limited computing and memory resources. To address the challenges, we propose Track-MM, a lightweight dual-memory framework for robust embodied visual tracking. Track-MM builds target-aware latent motion priors by leveraging pre-trained video foundation model representations via a lightweight target-aware motion adapter. The framework separately maintains appearance and motion memories with independent update gates to adapt to dynamic observations while retaining target identity. Benefiting from the lightweight design, Track-MM runs on resource-limited platforms with reduced parameters than mainstream methods. Evaluations on EVT-Bench demonstrate that our method strikes a favorable balance between tracking performance and computational efficiency. Real-world experiments further demonstrate promising zero-shot sim-toreal transfer, where the method maintains reliable tracking under varied lighting conditions on NVIDIA Orin platforms with practical deployment efficiency.