lightnav

{
  "model": "lightnav",
  "checkpoint": "/home/siqiteam/real_baseline_deploy/assets/final/lightnav_real/checkpoint",
  "ready": true,
  "busy": false,
  "episode": null,
  "worker": {
    "status": "ready",
    "model": "lightnav",
    "load_seconds": 24.980417925864458,
    "checkpoint": "/home/siqiteam/real_baseline_deploy/assets/final/lightnav_real/checkpoint",
    "load_count": 1,
    "device_name": "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
  },
  "robot_calibrated": true
}
Episodes ยท Download episode
{
  "schema": "real-baseline-v1",
  "model": "lightnav",
  "checkpoint": "/home/siqiteam/real_baseline_deploy/assets/final/lightnav_real/checkpoint",
  "epoch": 18,
  "step": 246474,
  "instruction": "go to the vending machine",
  "client_plan": [
    "go to the vending machine"
  ],
  "task_condition": "full original instruction; supplied stages logged only",
  "configuration": {
    "model": "lightnav",
    "project": "/home/siqiteam/real_baseline_deploy/final_service_state/project",
    "checkpoint": "/home/siqiteam/real_baseline_deploy/assets/final/lightnav_real/checkpoint",
    "epoch": 18,
    "step": 246474,
    "worker_python": "/home/siqiteam/real_baseline_deploy/envs/lightnav/bin/python",
    "host": "127.0.0.1",
    "ws_port": 5931,
    "http_port": 5930,
    "seed": 42,
    "wire_horizon": 12,
    "action_semantics": "body_increment_goals",
    "action_dt_s": null,
    "robot_calibrated": true,
    "require_timestamps": true,
    "log_root": "/home/siqiteam/real_baseline_deploy/lightnav/final_logs"
  },
  "worker": {
    "status": "ready",
    "model": "lightnav",
    "load_seconds": 25.280952685978264,
    "checkpoint": "/home/siqiteam/real_baseline_deploy/assets/final/lightnav_real/checkpoint",
    "load_count": 1,
    "device_name": "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
  },
  "status": "closed",
  "started_at": 1792327609.8304288,
  "units": "m_m_rad",
  "robot_execution_tested": false,
  "ended_at": 1792327621.8304515,
  "end_reason": "disconnect"
}

Inference 000000

Local predicted trajectory; measured poses are in trajectory.json.

model_input.jsonFile
{
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  "all_observations": [
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      "path": "/home/siqiteam/real_baseline_deploy/lightnav/final_logs/episode-20261018-204649-fa43880a/steps/000000/input-000.jpg",
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      "pose": [
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      "timestamp": 6022.770204727,
      "observation_id": 0
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  "time_resampled_indices": [
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  "model": "lightnav",
  "pose_conditioning": "not fed to these REAL adapters",
  "client_plan_used": false,
  "history_sampling": {
    "method": "nearest captured observation per occupied 4Hz bin; empty bins omitted",
    "max_capture_gap_s": 0.0,
    "unobserved_time_filled": false
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  "history_indices": [
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  "segments": [
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      "shape": [
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    "decoded": "<|im_start|>user\nYou are a mobile robot. You are given visual observations over time, ordered from earliest to most recent:<0.0 seconds><|vision_start|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|vision_end|>. Your assigned task is: <navigation_task>go to the vending machine</navigation_task>. You may be at the beginning, middle, or end of the task. Predict your future trajectory as a sequence of coarse-to-fine trajectory tokens.<|im_end|>\n<|im_start|>assistant\n",
    "tensor_shapes": {
      "input_ids": [
        1,
        193
      ],
      "attention_mask": [
        1,
        193
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      "pixel_values_videos": [
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      "video_grid_thw": [
        1,
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      "_original_video_grid_thw": [
        1,
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      "_post_vit_pool_factors": [
        1
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      "position_ids": [
        1,
        3,
        193
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      "mm_token_type_ids": [
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    }
  },
  "history_fps": 4,
  "model_history_refs": [
    {
      "path": "/home/siqiteam/real_baseline_deploy/lightnav/final_logs/episode-20261018-204649-fa43880a/steps/000000/input-000.jpg",
      "sha256": "bfe8c61afa43394fa416f49ce2e98aaeb92818c7c5ed38c118f6fc24c4b684cf",
      "pose": [
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      "timestamp": 6022.770204727,
      "observation_id": 0
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  ]
}
prompt.txtFile
<|im_start|>user
You are a mobile robot. You are given visual observations over time, ordered from earliest to most recent:<0.0 seconds><|vision_start|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|video_pad|><|vision_end|>. Your assigned task is: <navigation_task>go to the vending machine</navigation_task>. You may be at the beginning, middle, or end of the task. Predict your future trajectory as a sequence of coarse-to-fine trajectory tokens.<|im_end|>
<|im_start|>assistant
output.jsonFile
{
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    "stop": false,
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    "episode_id": "episode-20261018-204649-fa43880a",
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trajectory.jsonFile
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}
segment0.pt