Powered by elizaOSSimulation-first embodied data platform
Teleoperate robots.
From your browser.
No hardware. No expertise. Control simulated robot arms like a 3D web game, generate VLA-ready trajectory data, and own its provenance on Robinhood.
01 / The bottleneck
Physical AI is starving for demonstration data.
Language models scaled on internet text. Robot policies can't — every trajectory has to be demonstrated by a human. Today that means expensive robot rigs, slow lab pipelines, and datasets too small and too uniform to generalize. phyzical removes the hardware from the loop: anyone, anywhere, contributes manipulation demonstrations through a browser tab.
A browser is the only hardware a contributor needs.
Joint states, end-effector pose and object poses, every frame.
Isaac Sim re-rendering and domain randomization multiply every demo.
02 / Protocol
From browser tab
to training data.
The full loop takes minutes, not months. Every step below ships in the MVP — no future tense, no vaporware.
Pick a task
Browse the task board — sorting, pick-and-place, drawer opening. Each task shows difficulty, estimated time and reward before you start.
Teleoperate in-browser
A Unity WebGL scene loads in seconds. Drag the end-effector with your mouse; inverse kinematics solves the joints in real time over WebSocket sync.
Trajectory auto-recorded
While you work, the client samples joint positions, end-effector pose and object poses at 30–60Hz. Success criteria are checked live in the scene.
Submit, verify, own
One click submits the episode. Backend quality checks run, a Data ID is minted and anchored on Robinhood — provenance and rewards attach to you.
03 / Task board
Manipulation tasks,
gamified.
Block Sorting
Sort colored blocks into matching zones. Success on all blocks placed within tolerance.
Pick & Place
Grasp objects of varied geometry and place them on randomized targets.
Drawer Opening
Articulated manipulation — approach, grasp handle, pull along constrained axis.
Stacking Tower
Stack blocks into a stable tower. Physics-checked stability over 3 seconds.
Cable Routing
Deformable object manipulation. Route a cable through fixed guides.
Dual-Arm Handover
Coordinated two-arm transfer with grasp re-orientation mid-air.
Every completed episode enters the network queue: quality checks, augmentation, then on-chain registration. This preview shows the pipeline event format shipping with the MVP.
04 / Data pipeline
One human demo.
Hundreds of training samples.
Capture
Unity client records joint states, EE pose, object poses and user actions at fixed physics ticks. Buffered locally, submitted in one shot.
Quality check
Backend validation: task success criteria, trajectory smoothness, duration bounds, anomaly and replay-attack filters.
Augmentation
Accepted episodes are re-rendered in Isaac Sim with photorealistic lighting and domain randomization — one demo becomes hundreds of training samples.
Dataset
Versioned, filterable datasets exported in VLA / imitation-learning-ready formats for labs and model teams.
1{2 "episode_id": "EP_7f3a2c",3 "task_id": "block_sorting_01",4 "contributor": "0x94e1…c7f2",5 "sim": { "engine": "unity_webgl", "physics_hz": 60 },6 "duration_s": 45.2,7 "success": true,8 "trajectory": [9 {10 "t": 0.033,11 "joint_pos": [0.12, -0.87, 1.42, -0.55, 0.00, 0.31],12 "ee_pose": { "xyz": [0.42, 0.31, 0.87], "quat": [0, 0, 0.71, 0.71] },13 "gripper": 0.04,14 "object_poses": { "block_red_01": { "xyz": [0.40, 0.02, 0.90] } },15 "action": { "type": "ee_delta", "cmd": [0.01, 0.00, -0.02] }16 }17 ],18 "provenance": {19 "data_id": "phyz:robinhood:0x3d9f…a1b4",20 "content_hash": "sha256:e3b0c442…",21 "anchored_at": "block 24911072"22 }23}05 / Ecosystem
The fuel station for
agent robot stacks.
Agent frameworks like elizaOS now ship full robot stacks — simulation, RL training, real hardware bridges. What they need is human demonstration data. That's what phyzical produces.
Schema-aligned trajectories
phyzical episodes — joint_pos, ee_pose, actions and object poses at 30–60Hz — map directly onto eliza_robot's trajectory database. One converter, no re-collection.
Profile-matched tasks
Tasks are pinned to real robot profiles (RobotProfileId) — the same URDF/MJCF morphologies the elizaOS robot stack simulates and deploys, from arms to humanoids like AiNex and ASIMOV-1.
Warm-start training
Human demonstrations bootstrap imitation learning and offline RL — feeding continual-RL and text-conditioned policies instead of exploring from zero.
Browser demo in → trainable episode out
phyzical fuels embodied agents. elizaOS-ready by design.
06 / Provenance
Your data.
Verifiably yours.
Crowdsourced data is worthless without trust in where it came from. phyzical anchors each episode's identity, hash and contributor on Robinhood — so labs can audit datasets, and contributors keep a claim on the value they create.
Data ID on Robinhood
Every accepted episode gets a unique Data ID with its content hash anchored on Robinhood — cheap, fast, and verifiable by anyone.
Ownership follows the contributor
The wallet that produced the demonstration is written into the record. Provenance isn't a promise in a database — it's public state.
Foundation for a data economy
Verifiable origin is what makes data tradable. Registered episodes are the primitive for future licensing, incentives and marketplace flows.
07 / Roadmap
Shipping in phases.
Web Teleop Core
- Unity WebGL scene + URDF arm
- Mouse-drag end-effector + IK
- Task success detection
MVP Loop
- Next.js shell + wallet connect
- WebSocket state sync
- Task board + progress
Data & Chain
- Trajectory submit + QC pipeline
- Data ID registry on Robinhood
- Points rewards live
Scale & Augment
- Isaac Sim photoreal augmentation
- Domain randomization at scale
- Dataset access for labs