Editing Ai Robotics (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a robot manipulation learning system:

'''1. Data collection pipeline'''
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Task specification (language or structured)
    ↓
[Teleoperation data collection: human demonstrates task via VR/haptic interface]
[Target: 100–1000 demonstrations per task; diverse objects, positions, lighting]
    ↓
[Data annotation: label key states, contact events, success/failure]
    ↓
[RLDS (Robot Learning Dataset Specification) format for standardized storage]
    ↓
[Data augmentation: random crop, color jitter, background swap]
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'''2. Policy training (Diffusion Policy approach)'''
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Observation: RGB images (wrist + overhead camera) + proprioception (joint angles, gripper)
    ↓
[Visual encoder: ResNet or ViT → visual embeddings]
    ↓
[Diffusion policy: DDPM that denoises action trajectories conditioned on observation]
[Outputs: sequence of end-effector poses or joint angles (action chunking)]
    ↓
[Real robot execution: follow predicted trajectory with position controller]
    ↓
[Failure recovery: detect grasp failure via force sensor → retry]
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'''3. Deployment safety'''
* Workspace monitoring: 3D bounding box enforcement — robot cannot move outside safe zone
* Force/torque monitoring: if contact forces exceed threshold → stop and alert
* Human proximity detection: slow or stop when human enters workspace
* Emergency stop: dedicated hardware e-stop accessible at all times
* Gradual deployment: test in simulation → controlled lab → supervised real-world → unsupervised

[[Category:Artificial Intelligence]]
[[Category:Robotics]]
[[Category:Reinforcement Learning]]
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