README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - robotics
  - instruction-following
  - structured-generation
  - text-to-json
  - ros
  - ros2
  - sparse-transformer
  - embedded-ai
  - on-device
  - temporal-control
  - control-loop
pipeline_tag: text-generation
inference: false

Foros — Robotics Action Engine

Foros is an ultra-compact 10M parameter instruction-to-JSON model designed for low-latency, on-device robotics control. It translates plain-English robot commands — including temporal loops, timed sequences, and FSM transitions — directly into structured JSON arrays of operations compatible with ROS / ROS2.

Developed by AMEFORGE — https://huggingface.co/AMFORGE.
Built on the in-house SparseMind architecture (sparse token attention + sparse channel FFN + dynamic neuron typing).

---

Benchmark Results (Measured — Kaggle T4 GPU)

Evaluated on a 200-example robotics test suite (natural language → exact ROS JSON).

Model	JSON Valid (%)	Exact Match (%)	Latency (ms)	Size (MB)	Source
🚀 Foros — AMEFORGE (Ours)	95.5%	94.0%	345.4	38.3	Measured
❌ TinyLlama-1.1B	50.0%	10.0%	2448.1	2098.2	Measured
⚠️ Phi-3-Mini-3.8B	85.0%	55.0%	1250.0	7600.0	Literature Estimate

Key takeaways:

• Foros outperforms TinyLlama-1.1B by +84 percentage points in exact match on robotics commands.
• Foros is 7× faster than TinyLlama in response time.
• At 38.3 MB, Foros is 55× smaller than TinyLlama — runs on Jetson, Raspberry Pi, or any embedded CPU with no cloud dependency.

---

What it does

Atomic Commands

Natural Language Input	Structured Output (ROS JSON)
move to x=0.5 y=-1.2 z=0.8	[{"op":"move","x":0.5,"y":-1.2,"z":0.8}]
rotate joints to [0.0, 45.0, 90.0, 0.0, 0.0, 0.0]	[{"op":"joint_move","joints":[0.0,45.0,90.0,0.0,0.0,0.0]}]
close gripper with force 0.75	[{"op":"gripper","action":"close","force":0.75}]
wait for 3.5 seconds	[{"op":"wait","seconds":3.5}]
set velocity to 0.75 m/s	[{"op":"speed","velocity":0.75}]

Temporal / Loop Commands (v2)

Natural Language Input	Structured Output
repeat 5 times: move arm	[{"op":"repeat","times":5,"body":[{"op":"move",...}]}]
keep doing move arm until obstacle	[{"op":"repeat_until","cond":"obstacle","body":[...]}]
run control loop at 100Hz for 2.5 seconds	[{"op":"control_loop","frequency_hz":100,"duration_s":2.5,"body":[...]}]
every 0.5s do rotate joints for 4 steps	[{"op":"timed_seq","interval_s":0.5,"count":4,"body":[...]}]
simultaneously move arm and set speed	[{"op":"parallel","branches":[[...],[...]]}]

Complex Sequences (Multi-step planning)

Input:  pick up the red_box at 0.5 0.5 0.0 and place it at -0.5 1.0 0.0 =>
Output: [
  {"op":"move","x":0.5,"y":0.5,"z":0.0},
  {"op":"gripper","action":"close"},
  {"op":"move","x":0.5,"y":0.5,"z":0.3},
  {"op":"move","x":-0.5,"y":1.0,"z":0.3},
  {"op":"move","x":-0.5,"y":1.0,"z":0.0},
  {"op":"gripper","action":"open"}
]

---

Supported Operations (v2)

Category	Operations
Motion	move, joint_move, move_tcp, move_joint, home, trajectory
End Effector	gripper, tool, get_joint_values
Control Flow	wait, safety, stop, repeat, repeat_until
Temporal	timed_seq, control_loop, parallel, state_transition

---

Model Details

Property	Value
Architecture	SparseMind (decoder-only, sparse)
Parameters	10,347,395 (~10.3M)
Hidden size / Layers	256 / 6
Context length	384 tokens
Tokenizer	SparsForos Tokenizer (SentencePiece-BPE, vocab 3000)
Precision	FP32
Model Size	38.3 MB

---

Training Data

Trained on a rich hybrid dataset combining real execution data and synthetic temporal patterns:

Source	Type	Rows
Synthetic (AMEFORGE)	Atomic + temporal ROS/JSON commands	45,000
milistu/robot-instructions	Real function-call robot instructions	~887
jat-project/jat-dataset	Meta-World manipulation trajectories	~500
lerobot/pusht	Push-T manipulation action sequences	~400

---

Local Inference

import torch, sentencepiece as spm
from huggingface_hub import hf_hub_download

# Download
# Since the tokenizer repository is private, you must pass your HF_TOKEN
model_file = hf_hub_download(repo_id="AMFORGE/foros", filename="foros.pt")
tok_file   = hf_hub_download(repo_id="AMFORGE/foros_tok", filename="sparsforos_tokenizer.model", token="YOUR_HF_TOKEN")

# Tokenizer
sp = spm.SentencePieceProcessor()
sp.Load(tok_file)

# Model (import SparseMind & Config from training script)
from sparsemind_robotics_train import SparseMind, Config
ckpt  = torch.load(model_file, map_location="cpu", weights_only=False)
cfg   = Config(**{k: v for k, v in ckpt["config"].items() if k in Config.__dataclass_fields__})
model = SparseMind(cfg)
model.load_state_dict(ckpt["model"])
model.eval()

# Inference
prompt    = "move to x=0.5 y=-1.2 z=0.8 =>"
input_ids = torch.tensor([sp.EncodeAsIds(prompt)])
out_ids   = model.generate(input_ids, max_new=128, temp=1.0, top_k=1)
result    = sp.DecodeIds(out_ids[0, input_ids.shape[1]:].tolist())
print(result)  # [{"op":"move","x":0.5,"y":-1.2,"z":0.8}]

---

Citation

@misc{foros_robotics,
  title  = {Foros: An On-Device Instruction-to-JSON Engine for Robotics},
  author = {AMEFORGE},
  year   = {2026},
  note   = {Built on the SparseMind architecture. https://huggingface.co/AMFORGE}
}