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- NavThinker: Action-Conditioned World Models for Coupled Prediction and Planning in Social Navigation11
NavThinker is a future-aware framework that couples an action-conditioned world model with on-policy reinforcement learning, and shows state-of-the-art navigation success, with zero-shot transfer to Social-MP3D and real-world deployment on a Unitree Go2, validating generalization and practical applicability.
- Is Your Driving World Model an All-Around Player?5
WorldLens is introduced, a unified benchmark that measures world-model fidelity across the full spectrum, from pixel quality and 4D geometry to closed-loop driving and human perceptual alignment, through five complementary aspects and 24 standardized dimensions and forms a unified ecosystem for assessing generated worlds not merely by visual appeal, but by physical and behavioral fidelity.
- EventDrive: Event Cameras for Vision-Language Driving Intelligence2
Comprehensive evaluation across diverse tasks shows that event streams provide substantial gains in temporal precision, motion awareness, and robustness, bringing event sensing into the center of driving intelligence.
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- OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation–
OmniLiDAR is presented, a unified text-conditioned diffusion framework that generates LiDAR scans in a shared range-image representation across eight representative domains spanning three shift types: adverse weather, sensor-configuration changes, and cross-platform acquisition.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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