lamp/README.md

SimVerse / lamp

Mechanical-arm lamp targeting: choose one absolute angle for every joint of a fixed-base multi-segment arm so that the bulb at the tip illuminates a target point without any rod intersecting an obstacle.

• Records: 610 levels
• Modality: single rendered image (workspace with arm, target, obstacles)
• Output: {"actions": [{"joint": <int>, "angle": <int>}, ...]}

Loading

from datasets import load_dataset

ds = load_dataset("SimVer-ano/simverse2026", "lamp")
example = ds["test"][0]

# Prompt text (already in the record — no construction needed)
system_text = example["prompt"]["system"]
user_text   = example["prompt"]["user"]

# Image
image_path = example["images_relative_to_config"]["image"]   # e.g. "images/lamp-000.png"

# Gold answer
gold_actions = example["answer"]["actions"]                  # list of {joint, angle}

# Other useful task fields
print(example["arm"]["segmentCount"])       # number of joints
print(example["arm"]["angleStep"])           # allowed angle granularity
print(example["arm"]["angleMin"], example["arm"]["angleMax"])

Schema

Field	Type	Description
id	string	Sample id, e.g. "lamp-000"
__sample_id__	string	Same as id, exposed for HF loader convenience
prompt.system	string	The exact 5-section system prompt the benchmark uses
prompt.user	string	The exact 9-section user prompt for this level
arm.segmentCount	int	Number of arm segments (= number of joints)
arm.segments	list[{length}]	Lengths of each segment
arm.angleMin/Max/Step	int	Allowed angle range and step size
target	{x, y}	Target point coordinates
lamp.lightRadius	float	Coverage radius of the bulb
obstacles	list of obstacle objects	Striped wall blocks the rods must not intersect
images_relative_to_config.image	string	Image path relative to this config's root
answer.actions	list[{joint, angle}]	Reference solution; one known-valid joint configuration
legacy_answer	list[int]	Pre-v1 flat-array form of the answer (kept for back-compat; see SimVerse repo migration notes)

Solving by hand: minimal pipeline

import openai

def solve(example, model="gpt-5"):
    response = openai.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": example["prompt"]["system"]},
            {"role": "user", "content": [
                {"type": "text", "text": example["prompt"]["user"]},
                {"type": "image_url",
                 "image_url": {"url": f"file://{example['images_relative_to_config']['image']}"}},
            ]},
        ],
    )
    return response.choices[0].message.content

# The reply ends with "FINAL_JSON: {...}" — extract and parse:
import re, json
reply = solve(example)
final_json = json.loads(re.search(r"FINAL_JSON:\s*(\{.*\})", reply, re.DOTALL).group(1))

License

MIT — see LICENSE at the repo root.