chamber-bench-partner-sets
type: Dataset
description: The versioned CHAMBER-Bench v1.0 partner zoo: machine-readable rosters for every committed partner-set version (public members with committed parameter literals; private members with identity hashes only), rendered partner cards, per-set behavioural fingerprints, and the public learned members' checkpoint files. Partners are the frozen black-box teammates a benchmarked method is evaluated against.
conforms to: http://mlcommons.org/croissant/1.0
license: https://www.apache.org/licenses/LICENSE-2.0
version: 1.0.0
cite as: Safaei, F. (2026). CONCERTO and CHAMBER: Contact-rich Coordination with Opaque, Heterogeneous Teammates. Zenodo. https://doi.org/10.5281/zenodo.20128469
creator: Farhad Safaei
date published: 2026-07-06
data collection: Every record was generated in simulation (ManiSkill v3 / SAPIEN, plus one CPU diagnostic task) by the evaluation tooling committed in the fsafaei/concerto repository, under preregistered specifications locked with signed git tags before the runs. Each result bundle records the producing command, git commit, dependency-lock hash, platform fingerprint, and seed schedule, and passes the chamber-eval verify integrity and recomputation checks.
data collection type: Machine Generated
personal sensitive information: None. The data contains no people, no personal data, no customer or workplace data; every byte is simulator or tooling output.
data limitations: Simulation only (no real-robot data); exactly two agents per task; only the embodiments named in the task cards; measured heterogeneity effects on co-carry are null so far, so the data supports ad-hoc-teamwork competence claims, not heterogeneity-robustness claims; no vision-based partners in v1.0.
data biases: The zoo covers scripted impedance controllers, scripted presenters, and frozen jointly-trained policies on the listed tasks only; it does not represent human teammates or vision-based policies. Private members' parameters are deliberately withheld (published as SHA-256 identity hashes) so an unseen evaluation split stays meaningful.
data use cases: Instantiating the exact public partners behind a leaderboard row; evaluating new methods against the public split; auditing partner identity via hashes and fingerprints.
distribution: repo
distribution: manifest.json
distribution: SHA256SUMS.txt
distribution: README.md
distribution: rosters
distribution: cards
distribution: fingerprints
distribution: checkpoints

repo
type: FileObject
description: The Hugging Face dataset repository hosting this artifact.
encoding format: git+https
sha256: main

manifest.json
type: FileObject
description: File list with byte sizes and SHA-256 digests for every payload file.
contained in: repo
encoding format: application/json

SHA256SUMS.txt
type: FileObject
description: sha256sum -c checkable digest list covering every file including manifest.json.
contained in: repo
encoding format: text/plain

README.md
type: FileObject
description: The human-readable dataset card (plain language, licence, provenance, intended use, limitations).
contained in: repo
encoding format: text/markdown

rosters
type: FileSet
description: One JSON roster per partner-set version (set metadata, member construction boxes, split labels, identity hashes; public parameter literals).
contained in: repo
encoding format: application/json
includes: sets/*.json

cards
type: FileSet
description: Rendered per-member and per-set partner cards.
contained in: repo
encoding format: text/markdown
includes: cards/**/*.md

fingerprints
type: FileSet
description: Per-set behavioural fingerprint summaries rendered from the committed probe archives.
contained in: repo
encoding format: application/json
includes: fingerprints/*.json

checkpoints
type: FileSet
description: Public learned members' PyTorch checkpoints with JSON sidecars.
contained in: repo
encoding format: application/octet-stream
includes: checkpoints/*