oshd

Synchronized recordings from one rig: a Prophesee EVK4 (IMX636) event camera, a FLIR Blackfly S Firefly frame camera, an Intel RealSense D455 (color + depth), plus -- where recorded -- a Livox Mid-360 lidar + IMU, motion-capture poses and the robot's own control/state topics. Everything is recorded on a single host per trajectory, so every timestamp_ns within one trajectory shares one clock -- just match by nearest timestamp.

This dataset accumulates multiple recording sessions ("trajectories"), each with its own full set of sensor streams. Modality is the Hub config; a split is a group of trajectories (the table below lists each trajectory's split), and every row has a trajectory_id column. Files are stored one folder per trajectory, <modality>/<trajectory_id>/. Not every trajectory necessarily has every modality (a sensor can be off/disconnected for a given recording); see the table below for exactly which trajectory has which.

Trajectories

trajectory	split	mockup	light (K)	light intensity	EVK4 temp. (°C)	EVK4 illum. (lux)	events	firefly	realsense_color	realsense_depth	lidar	imu	mocap	robot_messages
cubsat_gotopose_com	train	—	—	—	—	—	141,407,497	4,000	859	1,505	—	—	—	—
cubsatagotopose	train	—	—	—	—	—	16,048,824	1,970	366	743	—	—	—	—
gopose_test_cubsat	train	—	—	—	—	—	12,630,096	2,192	406	825	—	—	—	—
gotoposecubsat_final	train	—	—	—	—	—	37,893,532	4,830	896	1,817	—	—	—	—
init_cubesat_test	train	—	—	—	—	—	93,883,279	9,487	3,414	3,534	—	—	—	—
test2cubestat	train	—	—	—	—	—	69,233,531	2,762	889	1,035	—	—	—	—
trajectory_001	train	—	—	—	—	—	442,986,965	420	—	—	—	—	—	—
trajectory_002	train	—	—	—	—	—	—	—	151	158	—	—	—	—
trajectory_003	train	—	—	—	—	—	1,350,684,260	459	172	178	—	—	—	—
trajectory_004	train	—	—	—	—	—	18,333,528	1,341	296	497	336	627	17,971	6,686
trajectory_006	train	—	—	—	—	—	25,127,922	1,635	300	607	409	700	17,883	9,909
trajectory_007	train	—	—	—	—	—	24,151,820	1,626	297	593	409	820	26,278	9,601
trajectory_008	train	—	—	—	—	—	126,755,280	1,634	302	609	391	918	18,897	10,417
trajectory_009	train	—	—	—	—	—	111,366,953	1,637	593	610	367	879	28,434	10,290
trajectory_010	train	antena	2750	80.2 %	—	—	486,453,454	1,633	1,218	568	4,218	517	23,015	10,617
trajectory_011	train	antena	2750	80.2 %	—	—	156,272,366	1,587	962	959	4,184	783	15,455	8,518
trajectory_012	train	antena	2750	80.2 %	—	—	93,737,710	1,674	987	995	4,446	826	29,621	8,719
trajectory_013	train	antena	6500	4 %	—	—	67,452,409	2,062	1,218	1,328	5,458	1,086	29,097	11,172
trajectory_014	train	antena	6500	4 %	—	—	34,068,143	1,600	998	1,032	4,200	805	22,659	8,484
trajectory_015	train	antena	6500	4 %	—	—	42,689,049	1,621	1,031	1,075	4,209	936	28,676	8,607
trajectory_016	train	antena	4600	22.5 %	—	—	480,019,395	1,634	1,212	820	4,179	660	26,586	10,493
trajectory_017	train	antena	5300	37.4 %	28 → 29	-1 → 0	390,671,823	1,719	1,191	596	433	722	17,879	9,867
trajectory_018	train	antena	5300	37.4 %	29 → 29	5 → -1	81,342,941	1,605	1,141	405	408	731	23,077	9,471
trajectory_019	train	antena	5300	37.4 %	30 → 29	4 → -1	167,188,586	1,618	1,082	245	222	851	23,105	9,048
trajectory_020	train	antena	2900	30.5 %	30 → 30	-1 → 5	77,744,376	1,597	1,053	1,044	237	829	30,160	11,066
trajectory_021	train	antena	2900	30.5 %	30 → 30	0 → 0	368,643,002	1,614	1,092	515	408	788	31,144	11,306
trajectory_022	train	antena	3550	77 %	30 → 30	0 → 0	481,431,494	1,602	1,010	717	409	782	23,017	10,822
trajectory_023	train	antena	5700	23.2 %	30 → 30	0 → 0	306,035,756	1,614	1,081	770	268	735	24,475	11,165
trajectory_024	train	antena	5700	23.2 %	30 → 30	0 → 0	270,454,616	1,829	1,202	959	299	935	19,810	10,049
trajectory_025	train	antena	5700	23.2 %	30 → 30	0 → 0	227,690,537	1,605	1,111	1,044	409	870	23,026	8,998
trajectory_026	train	antena	3050	92.6 %	30 → 30	0 → -1	464,337,872	1,616	1,102	68	409	854	20,602	9,248
trajectory_027	train	antena	3050	92.6 %	30 → 30	0 → 0	259,661,915	1,615	1,103	641	302	732	29,757	9,338
trajectory_028	train	antena	3050	92.6 %	30 → 30	0 → 0	371,430,694	1,620	1,116	86	317	650	19,772	9,260
trajectory_029	train	antena	6200	9 %	30 → 30	-1 → -1	220,011,885	1,613	1,117	585	327	899	24,936	9,269
trajectory_030	train	antena	6200	1.5 %	30 → 30	—	140,167,170	1,620	1,080	1,049	409	880	23,583	11,280
trajectory_031	train	antena	6200	1.5 %	30 → 30	—	209,121,215	1,614	1,043	867	409	782	25,867	9,919
trajectory_032	train	antena	6200	1.5 %	30 → 30	—	91,589,114	1,617	1,112	1,088	254	834	24,040	8,263
trajectory_033	train	antena	5500	6.5 %	30 → 30	—	73,849,269	1,621	1,141	1,162	270	849	25,984	9,372
trajectory_034	train	antena	5500	6.5 %	30 → 30	—	86,192,391	1,700	1,148	1,172	389	840	16,840	9,692
trajectory_035	train	antena	5500	6.5 %	30 → ?	—	234,392,931	1,620	1,109	762	410	784	25,202	9,592
trajectory_036	train	antena	5500	6.5 %	30 → 30	—	178,891,723	1,635	1,142	1,044	363	751	24,790	9,723
trajectory_037	train	antena	3450	31.4 %	30 → 30	—	261,336,705	1,633	1,152	776	275	805	31,444	9,179
trajectory_038	train	antena	4900	17.8 %	? → 30	—	136,197,777	1,622	1,139	1,075	339	750	24,007	9,243
trajectory_039	train	antena	3350	51.7 %	—	—	206,328,881	1,619	1,107	866	409	771	30,505	8,892
trajectory_040	train	antena_1_solar	5300	37.4 %	24 → 25	—	261,022,762	1,634	1,219	399	409	452	20,239	10,561
trajectory_041	train	antena_1_solar	5300	37.4 %	26 → 26	—	233,031,650	1,636	1,221	923	409	415	22,013	10,584
trajectory_042	train	antena_1_solar	5300	37.4 %	27 → 27	—	374,864,071	1,628	1,219	324	391	566	36,350	10,495
trajectory_043	train	antena_1_solar	5300	5.7 %	27 → 27	—	291,203,792	1,636	1,226	400	409	8,181	36,613	10,515
trajectory_044	train	antena_1_solar	5300	5.7 %	28 → 28	—	186,562,057	1,636	1,222	253	408	8,184	26,378	10,494
trajectory_045	train	antena_1_solar	5300	5.7 %	28 → ?	—	183,505,870	4,826	3,461	2,079	993	2,190	—	—
trajectory_046	train	antena_1_solar	5400	84.3 %	28 → 29	—	175,629,321	1,637	1,225	267	410	8,187	38,763	10,568
trajectory_047	train	antena_1_solar	5400	84.3 %	29 → 29	—	309,968,787	1,656	1,239	610	412	587	29,284	10,641
trajectory_048	train	antena_1_solar	6050	22.9 %	29 → 29	—	106,494,696	1,658	1,231	600	414	8,293	37,818	10,668
trajectory_049	train	antena_1_solar	4750	90.7 %	29 → 30	—	261,981,854	1,657	1,240	1,121	414	513	24,693	13,658
trajectory_050	train	antena_1_solar	2950	95.5 %	30 → 30	—	379,178,987	1,634	1,218	772	409	540	39,001	11,441
trajectory_051	train	antena_1_solar	4650	48.8 %	30 → 30	—	340,086,687	1,633	1,225	795	409	8,178	37,827	10,933
trajectory_052	train	antena_1_solar	4450	18.4 %	23 → 23	—	72,860,362	1,637	1,203	1,165	409	677	38,576	10,733
trajectory_053	train	antena_1_solar	4450	18.4 %	24 → 24	—	252,452,056	1,636	1,227	853	409	8,181	29,202	11,058
trajectory_054	train	antena_1_solar	4450	3.9 %	24 → 25	—	7,311,363	1,634	1,219	1,208	409	690	28,776	11,052
trajectory_055	train	antena_1_solar	4450	3.9 %	25 → 26	—	90,222,326	1,630	1,216	1,063	409	1,213	30,884	11,283
trajectory_056	train	antena_1_solar	4450	3.9 %	26 → 26	—	155,759,413	1,635	1,219	1,100	408	7,993	29,523	10,336
trajectory_057	train	antena_1_solar	3700	23.9 %	27 → 27	—	507,706,948	1,634	1,223	935	409	8,178	28,924	10,640
trajectory_058	train	antena_1_solar	3700	23.9 %	27 → 27	—	168,805,903	1,634	1,211	1,168	409	3,661	38,428	10,898
trajectory_059	train	antena_1_solar	3700	23.9 %	27 → 27	—	87,772,836	1,657	1,233	1,184	412	8,212	38,237	10,587
trajectory_060	train	antena_1_solar	3700	23.9 %	28 → 28	—	25,004,482	1,655	1,231	1,225	414	8,274	28,674	10,711
trajectory_061	train	antena_1_solar	3700	23.9 %	28 → 28	—	346,996,009	1,632	1,227	919	402	550	38,643	11,828
trajectory_062	train	antena_1_solar	5050	67.3 %	28 → 28	—	340,316,127	1,636	1,226	596	405	483	38,703	12,946
trajectory_063	train	antena_1_solar	5050	15.4 %	28 → 29	—	248,363,569	1,635	1,225	762	409	8,181	28,679	11,973
trajectory_064	train	antena_1_solar	5050	15.4 %	29 → 29	—	246,334,847	1,637	1,225	717	408	491	19,170	11,059
trajectory_065	train	antena_1_solar	5050	15.4 %	29 → 29	—	98,005,427	1,633	1,223	1,022	410	8,187	19,157	10,749
trajectory_066	train	antena_1_solar	5050	15.4 %	29 → 29	—	101,384,150	1,657	1,238	1,055	415	7,039	29,367	11,211
trajectory_067	train	antena_1_solar	6500	14.4 %	29 → 29	—	49,695,766	1,654	1,238	1,148	414	8,275	19,470	12,581
trajectory_068	train	antena_1_solar	6500	14.4 %	29 → 29	—	23,114,183	1,656	1,233	1,186	415	6,208	38,414	12,306
trajectory_069	train	antena_1_solar	6500	14.4 %	29 → 30	—	235,055,784	1,648	1,231	805	413	8,238	28,871	11,251
trajectory_070	train	antena_1_solar	6500	14.4 %	29 → 30	—	199,451,068	1,642	1,225	878	414	5,973	34,015	10,957
trajectory_071	train	antena_1_solar	6500	14.4 %	30 → 30	—	187,406,722	1,654	1,239	1,029	414	3,323	38,998	10,804
trajectory_072	train	antena_1_solar	3000	11.7 %	30 → 30	—	135,262,044	1,653	1,230	1,070	413	6,141	28,891	11,718
trajectory_073	train	antena_1_solar	3000	11.7 %	30 → 30	—	410,994,050	1,655	1,242	570	414	8,079	20,263	11,207
trajectory_074	train	antena_1_solar	3000	11.7 %	30 → 30	—	382,311,314	1,656	1,242	675	414	8,225	29,440	11,110
trajectory_075	train	antena_1_solar	3000	11.7 %	30 → 30	—	127,536,864	1,634	1,222	995	409	8,181	19,339	10,996
trajectory_076	train	antena_1_solar	3000	11.7 %	30 → 30	—	69,851,190	1,636	1,224	1,135	409	8,133	19,101	12,756
trajectory_077	train	antena_1_solar	3000	11.7 %	30 → 30	—	87,686,475	1,636	1,220	1,094	410	8,031	24,626	12,661
trajectory_078	train	antena_1_solar	3350	32.2 %	30 → 30	—	94,266,499	1,636	1,225	1,194	409	501	30,014	11,665
trajectory_079	train	antena_1_solar	3350	32.2 %	30 → 30	—	61,510,985	1,633	1,226	1,175	409	8,182	38,873	10,601
trajectory_080	train	antena_1_solar	3350	32.2 %	30 → 30	—	107,526,770	1,637	1,218	1,153	408	8,094	34,080	10,554
trajectory_081	train	antena_1_solar	3350	32.2 %	30 → 30	—	183,933,025	1,635	1,224	847	410	8,181	27,184	10,608
trajectory_082	train	antena_1_solar	3350	32.2 %	30 → 30	—	253,124,883	1,634	1,217	753	409	8,178	28,804	12,172
trajectory_083	train	antena_1_solar	4000	13 %	30 → 30	—	168,547,541	1,637	1,227	866	409	8,170	19,222	10,977
trajectory_084	train	antena_1_solar	4000	13 %	30 → 30	—	427,845,528	1,635	1,226	869	409	8,183	38,976	10,908
trajectory_085	train	antena_1_solar	4000	13 %	30 → 30	—	322,302,976	1,638	1,221	1,005	409	8,189	28,814	12,863
trajectory_086	train	antena_1_solar	4000	13 %	30 → 30	—	228,300,249	1,653	1,239	1,014	414	8,280	38,971	10,637
trajectory_087	train	antena_1_solar	6100	90 %	30 → 30	—	476,085,479	1,613	1,195	539	392	6,458	27,901	11,546
trajectory_088	train	antena_1_solar	6100	90 %	30 → 30	—	188,283,239	1,448	1,067	963	354	7,112	33,525	9,133
trajectory_089	train	antena_1_solar	6100	90 %	30 → 30	—	294,179,666	1,638	1,219	990	409	8,189	38,709	10,555
trajectory_090	train	antena_1_solar	6100	90 %	30 → 30	—	537,186,729	1,636	1,206	696	380	648	37,920	10,526
trajectory_091	train	antena_1_solar	6100	90 %	30 → 30	—	299,505,099	1,637	1,222	981	405	7,979	23,297	10,568
trajectory_092	train	antena_1_solar	3700	30 %	31 → 30	—	270,899,012	1,635	1,223	741	409	8,178	38,573	10,528
trajectory_093	train	antena_1_solar	3700	30 %	31 → 30	—	344,880,947	1,803	1,339	812	447	9,017	41,969	11,762
trajectory_094	train	antena_1_solar	3700	30 %	30 → 31	—	229,508,497	1,635	1,211	1,035	402	2,394	26,718	10,341
trajectory_095	train	antena_1_solar	3700	30 %	31 → 31	—	437,862,014	1,631	1,219	778	394	563	38,543	10,535
trajectory_096	train	antena_1_solar	3700	30 %	30 → 31	—	282,464,239	1,634	1,210	1,034	410	620	36,964	10,266
trajectory_097	train	antena_1_solar	3700	30 %	31 → 31	—	434,166,888	1,638	1,227	700	410	8,190	29,130	10,603
trajectory_098	train	antena_1_solar	3700	30 %	30 → 31	—	398,368,185	1,659	1,243	743	416	510	23,257	10,983

(— means that sensor recorded no data for that trajectory, or for the light columns that the light settings weren't recorded; counts are event rows / image frames. Light is the light source's colour temperature and intensity, set by hand and entered at recording time -- also in manifest.json's light; the light's pose is the light body in mocap. Mockup is the target mockup on the rig; its CAD is models/real/<mockup>/ (metres, origin at the pivot of its rigid body in mocap), and models/real/mockups.json maps each trajectory to its mockup -- with static_pose (position in m, orientation as quaternion xyzw, in the world frame of mocap) when the mockup stood still and its own mocap rows (body = target) are partial or missing: then use static_pose. EVK4 temp./illum. are the event sensor's on-chip temperature and illumination measurement, read off the camera when the recording started → stopped; also in manifest.json's event_sensor_monitor.)

Configs / how to load

from datasets import load_dataset

# one modality, every trajectory of a split (filter on trajectory_id):
events = load_dataset("r3m3c3/oshd", "events", split="train")
firefly = load_dataset("r3m3c3/oshd", "firefly", split="train")
rs_color = load_dataset("r3m3c3/oshd", "realsense_color", split="train")
rs_depth = load_dataset("r3m3c3/oshd", "realsense_depth", split="train")
lidar = load_dataset("r3m3c3/oshd", "lidar", split="train")
imu = load_dataset("r3m3c3/oshd", "imu", split="train")
mocap = load_dataset("r3m3c3/oshd", "mocap", split="train")
robot_messages = load_dataset("r3m3c3/oshd", "robot_messages", split="train")

# one trajectory: load its folder (only those files are downloaded)
events = load_dataset("r3m3c3/oshd", data_files="events/cubsat_gotopose_com/*.parquet", split="train")

Each image config yields rows with a decoded PIL image column plus a timestamp_ns column. events yields a flat table (Arrow/Parquet-backed) with one row per event.

• lidar: one row per scan -- scan_index, timestamp_ns, header_stamp_ns, bag_timestamp_ns, and one list column per point field (x, y, z, intensity, tag, line, timestamp for the Mid-360), all the same length within a row.
• imu: one row per sample -- orientation quaternion, angular velocity (rad/s), linear acceleration (g, the Livox driver's unit, not m/s^2).
• mocap: one row per pose -- body (tracked rigid body name), position (m) x, y, z and quaternion qx, qy, qz, qw in the mocap world frame. Filter by body for one object.
• robot_messages: every other recorded topic (controller commands, joint states, joystick, arm/disarm, ...) -- one row per message with topic, msgtype, the time columns and data, the full message as a JSON string (json.loads(row["data"])). Filter by topic.

timestamp_ns is always on the recording host's clock. Non-camera streams additionally carry header_stamp_ns (the publisher's own stamp, null if the message has none) and bag_timestamp_ns (receipt time at the recorder); timestamp_ns equals the header stamp when that stream's header clock agreed with the recorder's to within 1 s, else receipt time.

Timestamps

Every timestamp -- events' t column and every image config's timestamp_ns column -- is an int64 nanosecond Unix epoch timestamp, consistent within a trajectory (same recording host, same clock, no NTP/PTP sync needed) but not comparable across trajectories -- each recording session starts its own clock reference. To align modalities, find the nearest timestamp within the same trajectory_id only.

Event timestamps: the sensor's own internal clock is a free-running counter with no absolute epoch reference of its own. Each trajectory anchors it to epoch time independently, via an offset confirmed stable across two consecutive event packets at the start of that trajectory's recording (guards against a stale/buffered backlog burst on the first packet -- see bag_to_dataset/convert.py). Events' relative timing to each other is exact (decoded bit-for-bit from the sensor's raw EVT3 stream); the absolute anchor is accurate to within one packet's capture latency (a few milliseconds), the same order of uncertainty the other sensors' own timestamps carry.

Minimal example: events around a given frame

import numpy as np
from datasets import load_dataset

# one trajectory: load its folders (only those files are downloaded)
events = load_dataset("r3m3c3/oshd", data_files="events/cubsat_gotopose_com/*.parquet", split="train")
frames = load_dataset("r3m3c3/oshd", data_files="firefly/cubsat_gotopose_com/*.parquet", split="train")

t0 = frames[100]["timestamp_ns"]
t_col = np.asarray(events["t"])           # loads the t column as one array
lo, hi = np.searchsorted(t_col, [t0 - 10_000_000, t0 + 10_000_000])  # +/- 10ms
window = events[lo:hi]                     # dict of x, y, p, t lists for that window

(works the same for any other trajectory -- swap the folder name)

Calibration

See calibration/<trajectory_id>.yaml for that trajectory's per-camera intrinsics (K, D, R, P) -- kept per-trajectory rather than shared, in case the rig gets recalibrated between recording sessions. Each entry has a source field: rig calibration accepted <date> (the checkerboard / monitor calibration of the rig; these entries also give T_realsense_color_cam, the camera's pose in the RealSense colour optical frame, p_realsense_color = T @ p_cam, OpenCV optical frames, RealSense mounted upside down with its raw image as the reference), ros_camera_info (published live by the driver) or supplement:... (the driver didn't publish real intrinsics; filled in from a separate calibration pass instead). Check this before trusting a camera's numbers.

Sensors

sensor	model	modality
events	Prophesee EVK4 (IMX636)	event stream (EVT3)
firefly	FLIR Blackfly S BFS-U3-63S4C	RGB frames (debayered)
realsense_color	Intel RealSense D455	RGB frames
realsense_depth	Intel RealSense D455	depth (16-bit, aligned to color)
lidar	Livox Mid-360	3D point cloud scans
imu	Livox Mid-360 built-in IMU	orientation, angular velocity (rad/s), linear acceleration (g)
mocap	motion capture (vrpn_mocap)	6-DoF pose per tracked body
robot_messages	robot / controller / teleop topics	one row per ROS message, payload as JSON

Simulated data (sim_3d_*, sim_zerog_* configs)

Simulated counterpart of the real recordings in this repo, from the Space Robotics Bench (Isaac Sim), driven by trained go-to-pose policies. Same layout as the real data -- one folder per modality, one sub-folder per trajectory -- in two groups. Each config has one train split with all its trajectories; every row has a trajectory_id, and sim_manifest.json gives each trajectory's scenario (e.g. orbital/iss/earth).

group	trajectories	content
sim_3d_<modality>/	4325	orbital inspection in 3D: a free-flying chaser orbits a static space structure (procedural CubeSats, satellite mockup, Venus Express, Lunar Gateway, ISS) against an Earth or black-sky background
sim_zerog_<modality>/	2088	Zero-G lab digital twin: the planar Floating Platform inspects or docks to a satellite mockup / CubeSat held by the lab's wall or ceiling arm, or floating

Configs

modality	sim_3d	sim_zerog	content
rgb	sim_3d_rgb	sim_zerog_rgb	RGB frames: image, timestamp_ns, frame_index
depth	sim_3d_depth	sim_zerog_depth	depth along the optical axis, 16-bit, units of calibration depth.scale_m (mm, cm for far scenes; 0 = no return)
semantic	sim_3d_semantic	sim_zerog_semantic	8-bit class IDs: 0 background, 1 unlabelled, 2 target, 3 robot arm, 4 spacecraft
events	sim_3d_events	sim_zerog_events	t, x, y, p (p: 1 ON, 0 OFF), emulated IMX636 (v2e or openEB: calibration events.emulator), pixel-aligned with the camera
lidar	sim_3d_lidar	sim_zerog_lidar	one row per scan; list columns x, y, z, distance, channel in the lidar frame
imu	sim_3d_imu	sim_zerog_imu	robot IMU: orientation_*, angular_velocity_* (rad/s), linear_acceleration_* (m/s^2)
mocap	sim_3d_mocap	sim_zerog_mocap	ground-truth pose + twist per body: x,y,z, qx,qy,qz,qw, vx,vy,vz, wx,wy,wz (world frame)
joint_states	—	sim_zerog_joint_states	per articulation: position, velocity, effort lists (lab arms, platform)
steps	sim_3d_steps	sim_zerog_steps	one row per policy step: action, observation, reward, reward_terms, goal errors, task_state

Per trajectory: calibration/<trajectory>.yaml (pinhole intrinsics K, mount extrinsics; RGB, depth, semantic and events share one pixel-aligned camera) and metadata/<trajectory>.yaml (scenario parameters, rates, policy, tracking summary). sim_manifest.json lists every sim trajectory with its group, scenario, duration and row counts.

Loading

from datasets import load_dataset

# one modality, every trajectory of the group:
rgb = load_dataset("r3m3c3/oshd", "sim_3d_rgb", split="train")
# every trajectory of one scenario (scenarios: sim_manifest.json):
import json
from huggingface_hub import hf_hub_download
m = json.load(open(hf_hub_download("r3m3c3/oshd", "sim_manifest.json", repo_type="dataset")))
iss = [t for t, e in m["trajectories"].items() if e["scenario"] == "orbital/iss/earth"]
rgb_iss = load_dataset("r3m3c3/oshd", data_files=[f"sim_3d_rgb/{t}/*.parquet" for t in iss], split="train")
# one trajectory (only its files are downloaded):
events = load_dataset("r3m3c3/oshd", data_files="sim_3d_events/trajectory_hera2_000001/*.parquet", split="train")
joints = load_dataset("r3m3c3/oshd", data_files="sim_zerog_joint_states/trajectory_hera2_000007/*.parquet", split="train")

Time and frames

• Timestamps (timestamp_ns, event t) are simulation time in ns since the episode reset, not Unix epoch; shared by every modality of a trajectory.
• Rates: camera 20 Hz, lidar 10 Hz, mocap/IMU/joints 120 Hz, policy 10 Hz (exact values in metadata/<trajectory>.yaml, rates_hz).
• Quaternions are xyzw; world frame z-up at the environment origin.

The full trajectory list (group, scenario, duration) is in SIM_DATASET.md and sim_manifest.json.