Edge AI for Quadruped & Bipedal Locomotion: 500Hz Policy Inference on NVIDIA Jetson Orin with INT8 TensorRT

Edge AI for Quadruped Locomotion: 500Hz Policy Inference on Jetson Orin

Quadruped robots (such as the Unitree Go2, B2, and ANYmal) rely on Reinforcement Learning (RL) policies trained in massively parallel simulators. However, porting an RL policy trained on an RTX 4090 cluster to an embedded edge computer like the NVIDIA Jetson Orin Nano requires strict latency optimization.

#1. The Locomotion Control Loop

To stabilize a robot dog on slippery mud, rocky steps, or stairs, the policy must process:

The policy network outputs 12 target joint positions at 500 Hz (every 2 milliseconds).

#2. INT8 Quantization with TensorRT

Standard PyTorch models execute in 32-bit floating point (FP32), consuming ~12ms per forward pass on edge hardware. By compiling to INT8 using NVIDIA TensorRT:

import tensorrt as trt

def build_engine_int8(onnx_file_path, engine_file_path, calibrator):
    logger = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(logger)
    config = builder.create_builder_config()
    
    config.set_flag(trt.BuilderFlag.INT8)
    config.int8_calibrator = calibrator
    
    # Enable DLA (Deep Learning Accelerator) cores on Jetson Orin
    config.default_device_type = trt.DeviceType.DLA
    config.DLA_core = 0
    
    with open(onnx_file_path, 'rb') as f:
        parser = trt.OnnxParser(builder.create_network(), logger)
        parser.parse(f.read())
        
    engine = builder.build_serialized_network(network, config)
    with open(engine_file_path, 'wb') as f:
        f.write(engine)