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ilessio-aiflowlab/project_genesis

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

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Model Card

GENESIS -- ANIMA Module

Part of the ANIMA Perception Suite by Robot Flow Labs.

Architecture

TorchBCPolicy -- Behavioral Cloning MLP for 7-DoF robot manipulation.

Parameter	Value
Observation dim	7 (joint states)
Action dim	7 (7-DoF actions)
Hidden layers	[256, 256, 128]
Activation	ReLU
Dropout	0.1
Parameters	101,639

Training

Setting	Value
Dataset	smol-libero (HuggingFace LeRobot, 13,021 samples)
Split	90/5/5 (train/val/test)
Optimizer	AdamW (lr=3e-4, wd=1e-4)
Scheduler	Cosine annealing + 5% warmup
Precision	bf16
Hardware	NVIDIA L4 (23.7 GB)
Epochs	193 (early stopped, patience=10)
Best val_loss	0.4628 (epoch 183)
Test loss	0.4219
Training time	21 seconds
Seed	42

Exported Formats

Format	File	Use Case
PyTorch (.pth)	`pytorch/genesis_bc_v1.pth`	Training, fine-tuning
SafeTensors	`pytorch/genesis_bc_v1.safetensors`	Fast loading, safe
ONNX	`onnx/genesis_bc_v1.onnx`	Cross-platform inference
TensorRT FP16	`tensorrt/genesis_bc_v1_fp16.trt`	Edge deployment (Jetson/L4)
TensorRT FP32	`tensorrt/genesis_bc_v1_fp32.trt`	Full precision inference

Usage

python

from genesis.torch_policy import TorchBCPolicy

# Load from checkpoint
model, ckpt = TorchBCPolicy.from_checkpoint("pytorch/genesis_bc_v1.pth")

# Predict
import torch
obs = torch.randn(1, 7)  # 7-DoF joint state
action = model(obs)       # 7-DoF action output

Additional Files

—checkpoints/best.pth -- Full training checkpoint (model + optimizer + scheduler, for resume)
—configs/training.yaml -- Complete training configuration (reproducibility)
—logs/training_history.json -- Per-epoch loss curves
—logs/norm_stats.json -- Normalization statistics for inference

License

Apache 2.0 -- Robot Flow Labs / AIFLOW LABS LIMITED