robopark/dexjoco-delta-data

DexJoCo delta-action data for DICE-RL

Processed image-and-state demonstrations. Action layout: world position delta (3), world relative SO(3) rotation vector (3), absolute Allegro joint targets (16). This is 22 values for single-arm tasks; bimanual tasks concatenate right then left arm blocks for 44 values, preserving each recorded hand's joint ordering. Deltas use the measured observation at the same timestep. Both states and actions are min-max normalized; BC and RL share the same normalization statistics. Observation version: initial_object_pose_v1.

Task	Episodes	Transitions	State dimension	Action dimension
pinch_tongs	100	40469	31	22
pick_bucket	100	43581	38	22
hammer_nail	100	21563	38	22
click_mouse	100	32702	31	22
fold_glasses	100	53878	38	22
water_plant	100	27745	38	22
bimanual_assembly	100	54630	61	44
bimanual_hanoi	100	107764	50	44
bimanual_microwave_cook	100	68503	61	44
bimanual_photograph	100	34377	61	44
bimanual_unlock_ipad	100	41042	61	44

Each task contains ph_pretrain/train.npz, ph_finetune/train.npz, their normalization.npz files, and episodes.json. The manifest records SHA-256 hashes. RL rewards and terminals are 1 only at each retained demonstration's final frame. These are synthetic end-of-demo success labels, not replay-verified success times for the original recordings. No model checkpoints are included.

RGB images (all native cameras)

Single-arm episodes include front, wrist, ego_left, and ego_right. Bimanual episodes include ego, wrist_left, and wrist_right. The original MP4 files are preserved byte for byte, including their resolution. images.json maps each processed timestep to its exact source video frame; the same initial-prefix cut is used for states, delta actions, and images. Each task stores videos under videos/episode_XXXX/<camera>.mp4. On the Hub, videos.zip bundles every camera video for a task. The download script automatically extracts and verifies them, preserving the original bytes.

Use agent.dataset.dexjoco_image.DexJoCoImageDataset (BC) or DexJoCoImageQLearningDataset (offline RL) from the accompanying checkout. The loader returns RGB uint8 (B, history, 3 * cameras, height, width) in conditions["rgb"] and normalized robot proprioception (first 23 state columns for single-arm tasks, first 46 for bimanual tasks) in conditions["state"]. Task/object privileged state is excluded by default. Frames are decoded on demand, with bounded caches and exact PTS-based seeking. Choose cameras and image size at training time; defaults are front,wrist for single-arm tasks, all three cameras for bimanual tasks, and 224 x 224 pixels. The loader reads the published videos directly; no source-dataset download or offline image conversion is required.

Choose the number of published episodes used for training with max_n_episodes; all native camera views remain available.

Array format

Load NPZ files with numpy.load(path, allow_pickle=False).

Key	Shape	Meaning
states	(T, obs_dim)	Normalized observations, float32
actions	(T, action_dim)	Normalized pose deltas and absolute hand targets, float32
traj_lengths	(N,)	Episode lengths, int64; sum equals T
rewards, terminals	(T,)	Finetuning split only, float32

normalization.npz stores obs_min, obs_max, action_min, action_max, action_mode, normalized, and observation_version. Normalization is 2 * (x - minimum) / (maximum - minimum + 1e-6) - 1. Unnormalize actions before decoding them with the latest measured robot pose: target_xyz = observed_xyz + delta_xyz and R_target = Exp(delta_rotvec) @ R_observed. Hand targets stay absolute. The state and raw target quaternions use wxyz ordering.

Bimanual state begins [right_pose7, left_pose7, right_hand16, left_hand16]. Bimanual raw targets and decode_actions output instead use [right_pose7, right_hand16, left_pose7, left_hand16]. Before sending decoded targets to the simulator's policy-mode API, reorder them to [right_pose7, left_pose7, right_hand16, left_hand16].

For initial_object_pose_v1, object pose features describe the initial object poses, matching the original recordings. They do not track moving objects.

Use the accompanying DICE-RL checkout's bash script/download_dexjoco_hf.sh. See its README_DEXJOCO.md for installation and action decoding. Source recordings: DexJoCo.