ilessio-aiflowlab/project_agora

AGORA Planner v1 — ANIMA Multi-Robot Task Planner

Part of the ANIMA Robotics Suite by Robot Flow Labs.

Overview

AGORA (Unified STEM Memory Framework) is a Wave-5 coordination module for multi-robot collaboration. This checkpoint is a LoRA fine-tuned Qwen2.5-1.5B-Instruct model trained to assign tasks to heterogeneous robot teams based on capabilities, battery, location, and failure history.

Paper

RoboOS-NeXT: Toward Lifelong, Scalable, and Robust Multi-Robot Collaboration (arXiv:2510.26536) Huajie Tan et al., October 2025

Training

Parameter	Value
Base model	Qwen/Qwen2.5-1.5B-Instruct
Method	LoRA (r=16, alpha=32)
Training data	5000 synthetic planning examples
Eval data	200 examples
Epochs	3 (834 steps)
Batch size	6 per device × 3 grad accum
Learning rate	2e-4 (cosine + 5% warmup)
Precision	bf16
Hardware	3x NVIDIA L4 (23GB each)
Train loss	0.237
Train runtime	7338s (~2h)
Token accuracy	92%
Format valid rate	100%

Exported Formats

Format	Path	Size	Use Case
SafeTensors	pytorch/agora_planner_v1.safetensors	2.9 GB	Fast loading, safe
PyTorch (.pth)	pytorch/agora_planner_v1.pth	2.9 GB	Training, fine-tuning
ONNX	onnx/agora_planner_v1.onnx + .onnx.data	5.8 GB	Cross-platform inference
TensorRT FP16	tensorrt/agora_planner_v1_trt_fp16.engine	3.4 GB	Edge deployment (Jetson/L4)
TensorRT FP32	tensorrt/agora_planner_v1_trt_fp32.engine	6.7 GB	Full precision inference

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "ilessio-aiflowlab/project_agora",
    subfolder="pytorch",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "ilessio-aiflowlab/project_agora",
    subfolder="pytorch",
    trust_remote_code=True,
)

prompt = """You are AGORA, a multi-robot task planner.
Given robots: bot_a (manipulator, 90% battery), bot_b (mobile base, 60% battery)
Tasks: pick_object (requires manipulation), navigate_to_door (requires navigation)
Assign each task to the best robot."""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Files

├── README.md                              # This file
├── pytorch/
│   ├── agora_planner_v1.safetensors      # SafeTensors (2.9 GB)
│   ├── agora_planner_v1.pth              # PyTorch weights (2.9 GB)
│   ├── config.json                        # Model config
│   ├── tokenizer.json                     # Tokenizer
│   └── tokenizer_config.json
├── onnx/
│   ├── agora_planner_v1.onnx             # ONNX model (4 MB + external data)
│   └── agora_planner_v1.onnx.data        # ONNX external weights (5.8 GB)
├── tensorrt/
│   ├── agora_planner_v1_trt_fp16.engine  # TRT FP16 (3.4 GB)
│   └── agora_planner_v1_trt_fp32.engine  # TRT FP32 (6.7 GB)
├── configs/
│   ├── paper.toml                         # Paper-aligned config
│   └── training.toml                      # Training config
├── logs/
│   ├── training_metrics.json              # Final metrics
│   ├── planning_train.jsonl               # Training data (5000 examples)
│   └── planning_eval.jsonl                # Eval data (200 examples)
└── scripts/
    ├── train_planner.py                   # LoRA training script
    ├── eval_planner.py                    # Evaluation script
    ├── generate_planning_data.py          # Synthetic data generator
    └── export_all.py                      # Export pipeline

License

Apache 2.0 — Robot Flow Labs / AIFLOW LABS LIMITED