EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In In
Overview
Researchers from the Shanghai AI Laboratory, Zhejiang University, and Shanghai Jiao Tong University have developed a wearable system to capture human activity data for robot training. The system combines a head-mounted camera, motion trackers, and sensor-equipped gloves to record egocentric video, body motion, finger articulation, and tactile data. The resulting dataset, EgoExplorer, includes 100 hours of recordings and a movement-reconstruction pipeline to estimate whole-body motion. The study shows improvements in body-position error reduction and hand stability compared to baselines, though deployment on physical robots was not tested.
From Tech Xplore — Robotics
Event evidence and corrections
1 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · training_data_hours: 500000 hours · Basis not reported · Differing source assertions
“全球能用的机器人训练数据，截至2026年初，只有约50万小时。”
Exact source · revision 2Source owner not reported
Reported quantity · training_data_hours: 10000000 hours · Basis not reported · Differing source assertions
“要训练一个实用的具身智能机器人，至少需要1000万小时的数据，缺口超过99%。”
Exact source · revision 2Source owner not reported
Reported quantity · hours of human activity recorded: 100 hours · Basis not reported
“lenging. Researchers at the Shanghai AI Laboratory, Zhejiang University and Shanghai Jiao Tong University recently compiled a dataset containing 100 hours of human activity recorded with a new wearable system. The approach used to create the dataset, presented in an arXiv preprint , combines video from a wearer's viewpoint with reconstructed body movements, recorded finger movements, available tactile data and descri”
Exact source · revision 2science-x
Reported quantity · number of trackers used: 5 other · Basis not reported
“of this coordination." Overview of EgoExplorer. Credit: arXiv (2026). DOI: 10.48550/arxiv.2610.18872 Recording data while people complete tasks Zhou, Jiang and their colleagues developed a new wearable system that can capture data while humans are completing different activities. This system combines a head-mounted GoPro HERO13 camera with five VIVE Ultimate Trackers, sensor-equipped MANUS Metagloves or Wuji gloves,”
Exact source · revision 2science-x
Reported quantity · number of gloves used: 5 other · Basis not reported
“of this coordination." Overview of EgoExplorer. Credit: arXiv (2026). DOI: 10.48550/arxiv.2610.18872 Recording data while people complete tasks Zhou, Jiang and their colleagues developed a new wearable system that can capture data while humans are completing different activities. This system combines a head-mounted GoPro HERO13 camera with five VIVE Ultimate Trackers, sensor-equipped MANUS Metagloves or Wuji gloves,”
Exact source · revision 2science-x
Reported quantity · number of mounts and straps used: 1 other · Basis not reported
“and custom 3D-printed mounts and straps. The head-mounted camera records video from a wearer's viewpoint. The five trackers, one placed on a wearer's head, one at the back of each hand and one at each ankle, track their own position and orientation. The sensor-equipped gloves can record the movement of a wearer's fingers, with the Wuji gloves also recording tactile data. Finally, the mounts and straps were used to s”
Exact source · revision 2science-x
Reported quantity · number of human demonstration recordings used: 2 other · Basis not reported
“xperiments, jointly generating video, movement and touch data was found to produce better results than generating each separately. The researchers also trained a controller for a simulated Unitree G1 humanoid robot to follow movements adapted from two of the human demonstration recordings they collected. The simulated robot followed both movement sequences without falling, although this did not demonstrate precise ob”
Exact source · revision 2science-x
Evidence dependency changes (6)
- 2026-10-30T20:57:11.371Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T20:17:56.848Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T20:17:39.320Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T19:25:33.732Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T19:25:20.458Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T17:27:23.378Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
Report timeline
Follow attributed reports and material updates.
- Chinese robotics — Dataset and collection discoverySignalRobotics Training Data Gap and Emerging Data Collection Infrastructure
As of early 2026, global robot training data amounts to about 500,000 hours, far below the 10 million hours needed for practical embodied intelligence. Data collection is expensive and labor-intensive, with new dedicated hardware and infrastructure emerging to address these challenges. The article details recent data sets, training facilities, and policy support for the field.
- Tech Xplore — RoboticsSignalWearable system captures human activity data for robot training
Researchers from the Shanghai AI Laboratory, Zhejiang University, and Shanghai Jiao Tong University have developed a wearable system to capture human activity data for robot training. The system combines a head-mounted camera, motion trackers, and sensor-equipped gloves to record egocentric video, body motion, finger articulation, and tactile data. The resulting dataset, EgoExplorer, includes 100 hours of recordings and a movement-reconstruction pipeline to estimate whole-body motion. The study shows improvements in body-position error reduction and hand stability compared to baselines, though deployment on physical robots was not tested.
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