---
pretty_name: BiGym 2.0 Coding-Agent Rollouts
license: cc-by-4.0
language:
- en
task_categories:
- robotics
tags:
- robotics
- bigym
- humanoid
- loco-manipulation
- coding-agents
- evaluation
---
BiGym 2.0 — Coding-Agent Rollouts
Every evaluation episode behind the coding-agent results of **BiGym 2.0**:
the frozen program from each development session, replayed on all 100 hidden
evaluation seeds. Two agents (Claude Opus 5.5 via Claude Code, GPT-6 Astra via
Codex CLI), nine tasks, three sessions each: 54 programs, 5,400 episodes. Each
replay reproduces the scored outcome of its seed.
## Layout
```
manifest.csv one row per episode
episodes//_seed.npz simulator state and actions
viser//_seed.viser 3D replay for the viser player
viser//_seed.viser.json its frame count, length and camera
trace//_seed.json lines the program ran at each control step
```
Sessions are named `opus-s1` … `opus-s3` and `astra-s1` … `astra-s3`; seeds run
from 620000 to 620099.
## Episodes
One `.npz` per episode, stepped at the 50 Hz control rate (step *i* is at
*i*/50 s). No images are stored: the full simulator state is, so any camera
at any resolution can be re-rendered.
| Key | Shape | |
|---|---|---|
| `full_qpos` | (T+1, 47) | positions of the robot and every object |
| `full_qvel` | (T+1, 46) | velocities |
| `action` | (T, 21) | joint targets the program sent |
| `reward` | (T, 1) | per-step reward |
| `seed`, `length` | scalar | evaluation seed, T |
| `success`, `fell` | scalar | 1.0 if the goal state held for one second / if the robot fell |
| `termination` | scalar | `success`, `timeout`, `fell`, `physics_error` or `terminated` |
## Code traces
Which lines of its program each episode ran, step by step: the episode was
re-run with the frozen program under Python's `sys.settrace`, and a trace is
kept only if that re-run's state trajectory is identical to the scored one,
frame for frame. 5,347 of the 5,400 episodes have one. The other 53 (43 of
them `reach_target_multi_modal/opus-s1`) re-run with float drift from about
step 42: same length and outcome, up to a few millimetres apart.
Line numbers refer to the program as the project page lists it: `policy.py`,
then each helper module in name order, each after three extra lines (a blank line, a
`# ──────── ────────` divider, a blank line).
| Key | |
|---|---|
| `length`, `success` | episode length T, outcome |
| `sets` | the distinct sets of lines run in one step |
| `steps` | for each of the T steps, the index of its set in `sets` |
| `window`, `segments` | for display: phases of the episode, as `[from, to, line, [methods]]` over runs of the lines run in the last `window` steps |
## Manifest
`manifest.csv` has `task`, `session`, `model`, `harness`, `program_version`
(the development iteration that was frozen and scored), `seed`, `success`,
`length`, and the paths of the episode and its replay.
## Citation
```bibtex
@article{zhang2026bigym2,
title = {BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation},
author = {Zhang, Zexi and Zhu, Zecheng and Chen, Zidong and Tuya, Zulkhuu and James, Stephen},
journal = {arXiv preprint arXiv:2610.07594},
year = {2026}
}
```