Executive Summary
Step-by-step guide to compiling and deploying PyTorch RL locomotion policies to NVIDIA Jetson Orin at 500Hz with INT8 TensorRT quantization for dynamic terrain traversal.
Key Takeaways
- ✓The Locomotion Control Loop — covered in depth with practical examples, formulas, and code.
- ✓INT8 Quantization with TensorRT — covered in depth with practical examples, formulas, and code.
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)