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[0.4192,0.3969,0.3926,0.3903,0.4479,0.4458,0.4434,0.4346,0.4235,0.4277,0.436,0.404,0.4029,0.3559,0.3(...TRUNCATED) | [0.642,0.6449,0.6411,0.6388,0.6409,0.6493,0.6443,0.6519,0.6536,0.6461,0.6468,0.6746,0.6663,0.6581,0.(...TRUNCATED) | [0.4075,0.4011,0.3978,0.4066,0.4147,0.4128,0.4261,0.4316,0.4184,0.4303,0.4273,0.3865,0.4127,0.4145,0(...TRUNCATED) | [0.4778,0.4822,0.4589,0.4589,0.4767,0.4741,0.472,0.4728,0.4894,0.4919,0.4807,0.4458,0.4563,0.4536,0.(...TRUNCATED) | [0.2124,0.2133,0.211,0.208,0.2216,0.2199,0.2145,0.2193,0.2166,0.212,0.2132,0.2121,0.2113,0.2136,0.20(...TRUNCATED) | [0.2927,0.2878,0.2909,0.2918,0.2856,0.2847,0.294,0.2879,0.2918,0.2922,0.2906,0.2906,0.2883,0.2905,0.(...TRUNCATED) | [0.5703,0.5838,0.5708,0.584,0.5951,0.5836,0.5823,0.5897,0.5899,0.5939,0.587,0.5886,0.5906,0.5856,0.5(...TRUNCATED) | [0.2484,0.2452,0.2459,0.2502,0.2484,0.2477,0.2422,0.2415,0.2416,0.2424,0.2377,0.2423,0.2449,0.24,0.2(...TRUNCATED) | [0.2662,0.2685,0.2684,0.2639,0.2649,0.27,0.2668,0.2703,0.2713,0.2714,0.2739,0.2728,0.2747,0.28,0.266(...TRUNCATED) | [0.2725,0.2732,0.2776,0.2722,0.2723,0.2762,0.2792,0.2823,0.2769,0.2704,0.2717,0.271,0.269,0.2742,0.2(...TRUNCATED) | [0.3077,0.3067,0.2964,0.2889,0.2905,0.3,0.3134,0.3128,0.2989,0.2962,0.3104,0.3111,0.2964,0.3197,0.30(...TRUNCATED) | [0.2612,0.2564,0.2552,0.2615,0.2565,0.2592,0.2571,0.2633,0.2554,0.2526,0.2489,0.2535,0.2472,0.2469,0(...TRUNCATED) | [0.2606,0.2584,0.2579,0.2551,0.2569,0.2533,0.2584,0.2567,0.2543,0.2537,0.256,0.2531,0.2511,0.2507,0.(...TRUNCATED) | [0.6518,0.6554,0.65,0.6493,0.6505,0.6539,0.6539,0.6547,0.6553,0.6534,0.6526,0.6429,0.6556,0.6602,0.6(...TRUNCATED) | [0.668,0.6769,0.6724,0.6783,0.6737,0.6723,0.6747,0.6799,0.6764,0.6837,0.6783,0.6818,0.6758,0.6798,0.(...TRUNCATED) | [0.564,0.5726,0.552,0.5492,0.5538,0.5462,0.5603,0.5399,0.5456,0.5531,0.5579,0.5614,0.5511,0.5574,0.5(...TRUNCATED) | [0.3677,0.369,0.3716,0.3742,0.3757,0.3737,0.3738,0.3784,0.3685,0.3702,0.3756,0.371,0.3775,0.379,0.37(...TRUNCATED) | [0.2393,0.2419,0.2419,0.2383,0.2411,0.243,0.243,0.2448,0.2438,0.2395,0.239,0.2389,0.2465,0.2428,0.24(...TRUNCATED) | [0.3037,0.3066,0.3065,0.304,0.3023,0.3012,0.3024,0.3057,0.3075,0.3069,0.306,0.3056,0.3054,0.3042,0.3(...TRUNCATED) | [0.5551,0.5682,0.5782,0.5637,0.5875,0.5896,0.5908,0.6157,0.6052,0.5848,0.6017,0.6181,0.606,0.5738,0.(...TRUNCATED) | [0.6388,0.6445,0.6463,0.6455,0.6455,0.6269,0.6359,0.6151,0.6155,0.6155,0.5861,0.6283,0.6324,0.631,0.(...TRUNCATED) | [0.4712,0.4713,0.5304,0.5489,0.5197,0.5288,0.5272,0.53,0.5393,0.5351,0.5349,0.5058,0.4986,0.5036,0.4(...TRUNCATED) | [0.4522,0.4652,0.4677,0.469,0.4679,0.4762,0.485,0.4852,0.4646,0.4891,0.4807,0.4895,0.4859,0.4433,0.4(...TRUNCATED) | [0.3892,0.4103,0.3866,0.371,0.3706,0.3879,0.3873,0.3747,0.3958,0.3955,0.4148,0.407,0.4332,0.418,0.43(...TRUNCATED) | [0.6241,0.6129,0.5953,0.5955,0.5496,0.4945,0.4373,0.4247,0.442,0.4209,0.4501,0.4535,0.4476,0.4404,0.(...TRUNCATED) | [0.7453,0.736,0.7513,0.7487,0.7584,0.7227,0.7175,0.7136,0.6927,0.6912,0.6815,0.6505,0.6479,0.6302,0.(...TRUNCATED) | [0.2492,0.2432,0.2431,0.2423,0.2393,0.2391,0.2431,0.2436,0.2509,0.2504,0.2796,0.2822,0.2915,0.3091,0(...TRUNCATED) | [0.2821,0.2705,0.2704,0.2788,0.2671,0.2551,0.2538,0.2525,0.2473,0.2898,0.2652,0.2554,0.262,0.2646,0.(...TRUNCATED) | [0.306,0.3036,0.3046,0.3039,0.3042,0.3048,0.3028,0.3057,0.3039,0.3065,0.3088,0.307,0.3054,0.304,0.30(...TRUNCATED) | [0.7254,0.7256,0.7266,0.7254,0.7257,0.7253,0.7249,0.722,0.7225,0.7247,0.7248,0.7262,0.7295,0.7216,0.(...TRUNCATED) | [0.2922,0.2815,0.2848,0.278,0.2821,0.2622,0.2743,0.2842,0.2822,0.2849,0.2852,0.2643,0.273,0.2563,0.2(...TRUNCATED) | [0.3403,0.3355,0.3346,0.3314,0.3269,0.3254,0.3298,0.3257,0.3219,0.3303,0.3432,0.3224,0.3294,0.3305,0(...TRUNCATED) | [0.7413,0.7403,0.7371,0.7452,0.7478,0.7442,0.7436,0.7438,0.7464,0.7343,0.7363,0.745,0.7417,0.7377,0.(...TRUNCATED) | [0.4534,0.4553,0.4547,0.4572,0.4575,0.4621,0.455,0.4514,0.45,0.4473,0.4473,0.429,0.4154,0.4172,0.416(...TRUNCATED) | [0.4665,0.4658,0.4709,0.4631,0.4665,0.4619,0.4563,0.4512,0.4566,0.4497,0.4427,0.4441,0.4485,0.4439,0(...TRUNCATED) | [0.6686,0.6678,0.6608,0.6731,0.6617,0.6634,0.6618,0.6652,0.6719,0.6707,0.666,0.6665,0.6698,0.6721,0.(...TRUNCATED) | [0.5669,0.569,0.5712,0.5741,0.5713,0.569,0.5702,0.5684,0.5695,0.5694,0.572,0.5702,0.5691,0.568,0.569(...TRUNCATED) | [0.5969,0.5912,0.5974,0.5921,0.6002,0.6006,0.5965,0.5955,0.5906,0.5926,0.5915,0.5953,0.5999,0.6027,0(...TRUNCATED) | [0.5029,0.5029,0.5083,0.5095,0.5136,0.504,0.5009,0.5151,0.5066,0.5096,0.5114,0.5066,0.5061,0.5041,0.(...TRUNCATED) | [0.2693,0.2642,0.2684,0.2693,0.2718,0.2652,0.268,0.2627,0.2692,0.2696,0.2705,0.2679,0.2722,0.27,0.26(...TRUNCATED) |
rby1-fold-v1.1
Rainbow Robotics RB-Y1 T-shirt folding — DAgger correction set (2026-06-29) with per-frame advantage labels, LeRobot v3.0. Human-in-the-loop intervention rollouts collected on top of the rby1-fold-v1.0 policy, then hand-edited and recovered.
- 40 episodes / 81,813 frames @ 30 fps (~45.5 min)
- task (single, per-frame
task_index):fold the t-shirt(0) - cameras:
cam_high,cam_left_wrist,cam_right_wrist(480×640, AV1) - state / action: 26-dim — torso(6) + right_arm(7) + left_arm(7) + head(2) + wheel(2) + right_gripper(1) + left_gripper(1)
- every frame flagged
observation.is_intervention = true(expert-correction segments)
Relation to v1.0: v1.0 is the cleaned imitation master (126 ep). v1.1 is the next round of on-policy corrections — the human takeovers gathered while running the v1.0-trained policy, curated with dataset_segment_editor and re-encoded. Intended to be merged into / co-trained with v1.0 for AWBC-style fine-tuning.
Columns
observation.state, action, observation.images.*, observation.is_intervention, timestamp, frame_index, episode_index, index, task_index.
Labels (annotations/)
Advantage labels predicted by the RB-Y1 stage-advantage estimator (rby1_advantage_estimator_113_260627/step_010000), horizon 50, computed per frame (image-space, resolution-independent — aligned 1:1 with the frames here).
eval_progress.json—{episode_index: [stage_value_pred, ...]}, the estimator's per-frame stage-progress value V(s).advantage.json— per-framerelative_advantage= V(s+50) − V(s) andabsolute_advantage. For AWBC prompting, the global p70 threshold ofrelative_advantageis 0.032 (frames above →Advantage: positive).
Note
meta/stats.json numeric stats are computed from state/action; run compute_norm_stats before training. Videos are AV1 at native 480×640.
Changelog
- v1.1 (2026-06-29): DAgger correction set — 40 curated/recovered intervention episodes collected against the v1.0 policy, plus estimator advantage labels.
- see rby1-fold-v1.0 for the base imitation master and its lineage.
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