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/<task>/<session>_seed<seed>.npz simulator state and actions
viser/<task>/<session>_seed<seed>.viser 3D replay for the viser player
viser/<task>/<session>_seed<seed>.viser.json its frame count, length and camera
trace/<task>/<session>_seed<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
# ──────── <file> ──────── 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
@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}
}