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Environment datasheet · Robot manipulation

Upright slatted bench from 11 boards by a bimanual robot

ID llarms/task09 · revision 1 · tested by 2 November 2026

In a simulated robot workshop, the agent drives a two‑handed robot by typed commands to build two upright leg frames, each topped by a seat support, and join them with three seat slats: 11 boards from angled stock, each exactly in its outline, fixed with 42 screws.

Checks
1, 1 point for the whole build, none for part of it
Versions
Request only
Attempts
1: 1 model × 1 version, one each
Best, request only
0% (GPT‑6.1 Sol)
Full solves
0 of 1
Attempts with any reward
0 of 1
Median, request only
20 min
The job

An upright bench: 11 boards, 14 joints, 42 screws

Role
The controller of a simulated bimanual robot in a workshop of wooden boards, acting only through a local HTTP command service, with nobody giving movement advice.
Request
Pick up and place all 11 boards, turning them as needed, so every corner rests inside its outline, then fix all 14 joints with their 42 screws and back the drills out; check the measured result before stopping.
Deliverable
The final physical state, from the engine's last observation: 11 boards placed and released, 42 screws installed, all 14 joints rigid and the drills withdrawn. A short summary is saved too; its claims don't count.
What the reward depends on
Every board inside an outline of its own size once settled, after a real lift and release, all 14 joints rigid with three screws each, and the drills withdrawn. One point or none.
Where the requirements are
Stated in the request and one interface document, with the runtime's source open to read; each observation reports the targets and progress. Nothing is hidden: the difficulty is physical control.
The world

A contact‑only physics workshop, read as exact state and driven by typed commands

Interface
A Codex chat subagent running GPT‑6.1 Sol at its default reasoning setting, sending JSON to a local HTTP control server through its shell tools: read the state, send a step or a sequence of velocity segments, and post short progress notes.
Physics
Rigid‑body physics (cannon‑es) in its own headless Edge process at 180 Hz, apart from any viewer. Simulated time advances only during commands and pauses while the agent plans.
The robot
Two seven‑joint manipulators on bases that slide across the floor, each with a palm and five fingers of three joints, with spread and thumb opposition. Motion is rate‑limited and stopped by collisions, so a target isn't always reached.
Grip
No attachment constraint: a hold is opposed, force‑bearing finger contact, so boards, drills and screws can slip, turn or drop while the hand stays closed, and over‑curling can push a board out.
Observations
Exact structured state, not images: board poses, sizes and velocities; drill and screw poses and states; finger angles, contact force and slip; collision and joint‑limit feedback; each target's pose and progress.
The build
On each side a foot runner, two upright legs and a seat support, then three seat slats across: 11 boards from angled stock, joined at 14 overlaps; unsupported pieces need bracing while fixed.
Drill and screws
Graspable drills with no magazine, and screws handled one at a time: pinch a screw, set it on the overlap, align the other hand's drill bit, engage, let go, feed along the axis, then withdraw before any sideways move.
Recording
Every command result and sampled pose is saved; four fixed cameras render review videos afterwards, at simulation speed. The saved observations, not the videos, are the record.
Network
Internet on, for the pinned physics libraries, and web tools available; nothing the job needs is online.
Limits
One session of 20 minutes of wall time, planning included, plus the scenario's own action and simulation‑time budgets. The request rules out resets, teleporting, direct state changes and earlier trajectories.
The reward

One all‑or‑nothing check on the final physical state

Structure
One check, worth one point: the whole build complete. Boards placed, screws installed and each board's worst corner protrusion are reported as diagnostics, never scored.
Placement
Each board must be lifted, released and left to settle with all eight corners inside a target outline of its own size, allowing only a numerical margin. Holding a board in place doesn't count.
Fastening
Every joint rigid, made by three non‑collinear screws driven through the top board into the one below, and every drill withdrawn. Matching the outlines alone doesn't pass.
Who decides
A script reads the final observation and transforms every board's eight corners into its target's frame; it also needs the engine's own success flag for lift, release, settling and any joints. No model judge, and the agent's claims don't count.
The rule in the request
The request stated that every corner must lie inside its outline, with the margin for rounding only, so this attempt was told the strict rule.
Engine reward
After each action the engine also returns a shaped reward from the boards' distances to their targets, with a bonus on success and a penalty for a lost board; the grader ignores it.
Formula
Reward = 1 if the build is complete, else 0. An untouched scene, a partial build or a missing final state scores 0.
Results

Every screw it pinched slipped out on the next lift

GPT‑6.1 Sol, the only model tested on the builds, scored 0%. It moved slats, runners and legs, losing grips on the way, and held the drill from early on, but each of the four screws it pinched slipped on the next lift. No board ended inside its outline.

VersionMedianFully solved
Request only1 model, one attempt each0%0 of 1

Each dot is one attempt; the line marks the median.

ModelRequest only
GPT‑6.1 SolOpenAI0%

One attempt, by GPT‑6.1 Sol at seed 1; no other model has been tested on the ten builds.

Made on 2 November 2026 on its own control server and physics process, told the strict rule in the request. Its closing summary called the build incomplete, as the grader found.

Failures

What the model got wrong

One attempt, by GPT‑6.1 Sol, told the strict rule; command numbers are from its saved sequence.

  1. Grips lost in transport. Commands 3 to 26 extracted slats, runners, legs and a seat support, with dropped grips and repeated collisions with stock.

  2. Screws slipped on lifting. Holding the drill from command 28, it pinched a screw at commands 69, 109, 148 and 187; each grasp was gone after the next lift.

  3. Half the commands on fine approaches. Commands 193 to 383 were short screw approaches and finger moves, with no further screw grasp.

  4. Nothing in place. At the 20‑minute limit, 0 of 11 boards were inside their outlines, the closest 259 mm out, and 0 of 42 screws were in.

Cost

What an attempt costs

Length
The attempt used its full 20 minutes of wall time, with 383 motion commands, many of them short approaches, and 252 simulated seconds. Model calls, tool calls and tokens weren't recorded; a motion command isn't a model call.
Cost
Not recorded: the attempt ran on a subscription sign‑in, which logs no cost or tokens, so we give no figure.
Licence

What you license

With a licence
Revision 1: the frozen runtime with this scenario at seed 1, the request and interface document, the grader and its self‑test, and the attempt's commands, observations, pose trajectory and four review videos, as baseline results.
Format
Runs on Node.js, with the physics in headless Microsoft Edge driven by Playwright, and a Python grader; today on a Windows PC, outside a container. Other packaging on request.
Fresh variants
Other builds and layouts in the same design, on request. They're never published.
Public or not
The AI Agent Trials, our public benchmark, sums up the job and shows each model's score, so hold out fresh variants for evaluation. The files, grader and answer key aren't published.
Known limitations

What we haven't measured, and what's uneven

Try a sample environment

Three are downloadable as Harbor tasks, with graders and reference solutions. Request one with your work email, or book a call about the other 74.