VLA (Vision-Language-Action) model architectures have converged. The competitive moat is shifting from model design to data quality and scale. Embodied intelligence has been named a national strategic industry, and data training ground standards are now in development.
No single data pathway can independently support general embodied intelligence. The future demands a hybrid approach — combining the precision of teleoperation, the scale of simulation, the diversity of human video, and the flexibility of UMI.
No single path can support general embodied intelligence alone. We provide four parallel pathways — flexibly combined for any scenario.
Human operators remotely control robots to perform tasks, capturing high-fidelity data with vision, force feedback, and joint trajectories. Supports ALOHA dual-arm control and VR teleoperation.
Build digital twins of the physical world where virtual robots train around the clock. Covers 31+ grasp types with varied lighting, materials, and scenes — generating billions of data points per week.
Real workers record task videos using smart glasses or motion-capture gloves during daily work — no production interruption. Open data sources, diverse scenarios, with proven log-linear scaling laws.
Portable handheld grippers with cameras decouple data collection from specific robot hardware. The same dataset trains multiple robot arms — enabling distributed "data factory" crowdsourcing.
Leveraging the BexByte Data Service Platform's annotation workbench and QC system — deeply adapted for embodied intelligence scenarios.
LiDAR/depth camera point cloud instance segmentation, semantic labeling, 3D bounding boxes — covering grasp points and obstacle detection.
Keyframe annotation, joint angle sequences, end-effector trajectories, contact force labels — covering the full manipulation chain.
VLA triplet annotation, visuo-tactile spatiotemporal alignment, scene description and instruction labeling.
Different collection methods serve different training stages. We design complete multi-stage training pipelines.
Massive internet human video data teaches the model "what the world looks like" and "how humans act." Egocentric video + motion-capture data builds action-scene priors.
Bridging general to specialized: simulation data, UMI collection, and limited teleoperation distill human commonsense into robot action spaces — completing Sim2Real transfer.
Requires isomorphic real-robot hi-fi data — with force, tactile, and friction cues — for fine-tuning and reinforcement learning. Pursuing task alignment, precision, and safety guarantees.
Desk tidying, kitchen tasks, laundry folding
Assembly, quality inspection, flexible loading
Surgical assistance, rehab training, pharmacy
Picking, palletizing, vehicle dispatch
Not locked into a single collection path. We flexibly combine all four pathways based on your training stage and task requirements — finding the optimal balance of quality, scale, and cost.
Embodied data annotation is deeply integrated with the BexByte Data Service Platform — annotation workbench, QC system, and export modules all shared. No redundant infrastructure.
Pay only when data quality meets the bar. Quantified metrics — grasp success rate, trajectory accuracy, Sim2Real transfer rate — ensure every dollar delivers measurable ROI.
Whatever stage you're at, our engineering team will evaluate the optimal data pathway combination for your needs.