Скачать или смотреть Robotics Build, Learning - 13

Vision, Gestures, Memory, and the Push Toward Full GPU Acceleration

🔥 Today’s Build:
This project evolves my Maya Vision Assistant into a more perceptive, interactive system — combining computer vision, gesture recognition, and memory-based responses inside a threaded, real-time loop.

This version still runs on partial CPU + partial GPU, which means there is visible lag — and that’s intentional. It marks the transition phase before moving the entire pipeline fully onto CUDA.

🧠 Learning Focus:
This build brings together:
• 👁️ Real-time vision (OpenCV + YOLO)
• ✋ Gesture recognition with landmark tracking
• 🧠 Short-term memory integrated into responses
• 🔄 Threaded architecture so the UI doesn’t freeze
• ⚡ GPU-enabled PyTorch available but not fully implemented yet

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⚙️ What Worked Today

Despite running on CPU-heavy inference + partial GPU support:
• YOLO detected objects correctly
• Gesture recognition streamed at steady FPS
• Voice tone analysis responded smoothly
• Whisper STT worked accurately
• TTS output was stable
• The interaction loop held together
• Memory system responded to context

This proves the multi-modal pipeline is structurally sound.

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⚠️ What Caused the Lag (and Why It’s Expected)

This version was NOT fully GPU-accelerated because:
• ONNX Runtime couldn’t load CUDA provider (missing cuDNN + CUDA DLLs)
• FER+ ran on CPU
• YOLO fallback path still used CPU
• MediaPipe delegates were removed but GPU models weren’t fully deployed yet

Meaning:
The system was doing all vision inference on CPU while I was preparing the GPU backend.

That’s why the lag exists.
And that’s exactly why the next build matters.

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🔧 What I Fixed / Built Today
• Repaired PyCharm environment
• Installed GPU PyTorch (confirmed working)
• Debugged ONNX CUDA failure (cublasLt64_12.dll missing)
• Repaired FER+ download issue via HuggingFace mirror
• Cleaned gesture detection loops
• Activated internal reasoning layer
• Added smooth threading to avoid UI freeze

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🚀 Next Build Will Be Fully GPU, Including:
• CUDA 12 installation
• cuDNN 9 integration
• ONNX Runtime GPU provider
• GPU-based facial expression (FER+)
• GPU-based YOLO inference
• GPU-based gesture models
• TensorRT acceleration

This will eliminate lag and unlock real-time multimodal intelligence.

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🎓 Learning Insights:
• Partial GPU is not enough — full CUDA stack is required
• ONNX GPU failures always trace to missing DLLs
• Memory integration massively improves interaction quality
• Gesture tracking must be optimized before moving fully to GPU
• Threading fixes UX, but inference speed requires CUDA

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