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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

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01mhqin /vln_n1_traintabular100K<n<1M0 likes371 downloads2mo agoHugging Face02SongzeLi /SID-VLN Datasets of Learning Goal-Oriented Language-Guided Navigation with Self-Improving Demonstrations at Scale. tabular1K<n<10K0 likes177 downloads1y agoHugging Face03oscarqjh /VLN-CE-R2R_easi VLN-CE R2R Dataset for EASI Vision-and-Language Navigation in Continuous Environments (VLN-CE) Room-to-Room (R2R) benchmark, repackaged for the EASI evaluation framework. Task An agent receives a natural language navigation instruction and must navigate through a Matterport3D indoor environment to reach a goal location. The agent uses discrete actions: STOP, MOVE_FORWARD (0.25m), TURN_LEFT (15 deg), TURN_RIGHT (15 deg). Success is measured when the agent stops within 3.0m… See the full description on the dataset page: https://huggingface.co/datasets/oscarqjh/VLN-CE-R2R_easi.3drobotics1K<n<10K0 likes154 downloads7mo agoHugging Face04KevinConnorLee /vln_n1_tensorstabular100K<n<1M0 likes94 downloads3mo agoHugging Face05oscarqjh /VLN-CE-RxR_easi VLN-CE RxR Dataset for EASI Vision-and-Language Navigation in Continuous Environments (VLN-CE) Room-across-Room (RxR) benchmark, repackaged for the EASI evaluation framework. Task An agent receives a natural language navigation instruction (in English, Hindi, or Telugu) and must navigate through a Matterport3D indoor environment to reach a goal location. The agent uses discrete actions: STOP, MOVE_FORWARD (0.25m), TURN_LEFT (30 deg), TURN_RIGHT (30 deg), LOOK_UP (30 deg)… See the full description on the dataset page: https://huggingface.co/datasets/oscarqjh/VLN-CE-RxR_easi.3drobotics10K<n<100K0 likes67 downloads7mo agoHugging Face06Rithvik762 /vln-trajectory-memory-stage2 VLN Trajectory-Memory — Stage 2 (projector alignment) Text-only question answering where the only source of truth is a robot's action history. Each record gives a navigation trajectory as a list of primitive actions and asks something that can only be answered by tracking where those actions lead: how far from the start, which way the robot faces, what happened in the last quarter of the route. It was built to measure whether a frozen vision-language model (Qwen3-VL-2B) can read… See the full description on the dataset page: https://huggingface.co/datasets/Rithvik762/vln-trajectory-memory-stage2.tabularquestion-answering100K<n<1M0 likes39 downloads2d agoHugging Face

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