pretty_name: >-
GR00T-N1.7 LIBERO-10 backbone features + K=10 action samples (normal +
occluded)
task_categories:
- robotics
modalities:
- video
- tabular
size_categories:
- n<1K
tags:
- robotics
- vision-language-action
- libero
- gr00t
- representation-probing
- occlusion-robustness
- failure-detection
GR00T-N1.7 LIBERO-10 — backbone features + K=10 action samples
Aligned rollouts of the stock NVIDIA GR00T-N1.7-LIBERO (libero_10
checkpoint) policy on LIBERO libero_10, in two matched scene variants
(normal and the LIBERO-Occ occluded suite), with K = 10 action chunks
sampled at every policy inference. Sample 0 is the chunk the robot executes;
the other 9 are drawn from the same observation and never executed. This is
what sampling-based failure detectors such as STAC need, alongside the
per-token backbone features used by representation probes (e.g. SAFE).
10 tasks × 2 variants × 25 initial states = 500 episodes, all with features and all 10 samples.
This is a separate run of the same 500 initial states (seed 7) as
podolinsky/gr00t-n1.7-libero-10-features,
which has one chunk per inference plus Gemini labels. GR00T's flow-matching head is
stochastic, so outcomes differ between the two runs: 412 of 500 episodes have
the same success label. The Gemini labels here (see Labels) come
from that new run, with a newer prompt and a finer failure taxonomy than v1.
Benchmarks & model
- LIBERO — Liu et al., arXiv:2306.03310
- LIBERO-Occ — Li et al., arXiv:2606.10862
- GR00T N1 — NVIDIA, arXiv:2503.14734.
Checkpoint
nvidia/GR00T-N1.7-LIBERO(libero_10), VLM backbonenvidia/Cosmos-Reason2-2B. - STAC — Agia et al., Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress, arXiv:2410.04640
Success rates
replan_steps = 8 (GR00T predicts a 16-step chunk, 8 are executed per
inference), max_steps = 520, seed 7. Success = the LIBERO BDDL goal
predicate; every failure is unsatisfied_goal and runs to the 520-step cap.
| # | LIBERO-10 task | normal | occluded | Δ |
|---|---|---|---|---|
| 0 | KITCHEN_SCENE3 turn on the stove and put the moka pot on it |
100% | 44% | −56 |
| 1 | KITCHEN_SCENE4 put the black bowl in the bottom drawer of the cabinet and close it |
88% | 36% | −52 |
| 2 | KITCHEN_SCENE6 put the yellow and white mug in the microwave and close it |
68% | 20% | −48 |
| 3 | KITCHEN_SCENE8 put both moka pots on the stove |
60% | 0% | −60 |
| 4 | LIVING_ROOM_SCENE1 put both the alphabet soup and the cream cheese box in the basket |
100% | 80% | −20 |
| 5 | LIVING_ROOM_SCENE2 put both the alphabet soup and the tomato sauce in the basket |
92% | 88% | −4 |
| 6 | LIVING_ROOM_SCENE2 put both the cream cheese box and the butter in the basket |
100% | 32% | −68 |
| 7 | LIVING_ROOM_SCENE5 put the white mug on the left plate and put the yellow and white mug on the right plate |
80% | 40% | −40 |
| 8 | LIVING_ROOM_SCENE6 put the white mug on the plate and put the chocolate pudding to the right of the plate |
92% | 28% | −64 |
| 9 | STUDY_SCENE1 pick up the book and place it in the back compartment of the caddy |
100% | 76% | −24 |
| all 10 (250 paired episodes) | 88.0% (220/250) | 44.4% (111/250) | −43.6 |
115 of the 139 occluded failures are occlusion-only (the matched normal
episode from the same initial state succeeds).
Layout
<scene_variant>/<NN>_<task_stem>/ep<NNN>/
rollout.json metadata + per-policy clock records
rollout.npz the arrays below
rollout.mp4 agentview video, 20 fps, one frame per control step
wrist.mp4 eye-in-hand video, same timing
labels.json Gemini video description + failure localization (see Labels)
labels.npz the same phrases / failure fields as aligned arrays
example.md human-readable render of labels.json
manifest.csv one row per episode
EXAMPLES.md all per-episode example.md cards in one file
failure_overview.png failure-mode share + onset time (see Failure statistics)
FAILURE_ACTION_SUMMARIES.md one row per failure: reason / recovery / prevention summaries
gemini_prompts.md verbatim Gemini prompt templates (+ gemini_prompts.json)
<NN> is the 1-indexed task id; <scene_variant> is normal or occluded.
500 episodes, ≈9.3 GiB (npz 9.2 GiB, 7.8–29.1 MiB each; videos 0.1 GiB);
21,934 policy inferences, 174,358 control steps.
rollout.npz
Action samples (the addition over the v1 dataset):
| key | shape | |
|---|---|---|
sampled_action_chunks |
(n_policy, 10, 16, 7) float32 |
all K = 10 raw chunks per inference, same observation |
predicted_action_chunks |
(n_policy, 16, 7) float32 |
the executed chunk, == sampled_action_chunks[:, 0] |
num_action_samples |
() |
10 |
executed_sample_index |
() |
0 |
Chunks are the raw GR00T output (delta-EEF x,y,z,roll,pitch,yaw,gripper).
The first replan_steps = 8 rows of sample 0 are executed.
Features, captured from the inference that produced sample 0 — the
layer-16 residual stream of the Cosmos-Reason2-2B backbone
(select_layer = 16 of 28), raw per-token, float16, stacked over the
n_policy inferences:
| key | shape | tokens |
|---|---|---|
base_image |
(n_policy, 64, 2048) |
agentview, 8×8 after 2×2 merge |
wrist_image |
(n_policy, 64, 2048) |
eye-in-hand, same |
language |
(n_policy, 200, 2048) |
instruction tokens, zero-padded to 200 |
language_mask |
(n_policy, 200) bool |
real vs padding |
language_len |
(n_policy,) int32 |
real instruction token count |
state_features |
(n_policy, 1536) |
the action head's embedded proprioceptive vector |
Executed actions and clocks, per control step (n_control,):
executed_actions (n_control, 7), control_step, sim_step (includes the
num_steps_wait = 10 settle steps, not in the video), policy_step,
chunk_index, video_frame_id (== control_step).
Scalars: success, replan_steps (8), n_policy, n_control,
img_tokens (64), hidden (2048), control_hz (20), has_features (True).
Alignment contract
Identical to the v1 dataset: policy_step ids are sequential, each maps to a
contiguous block of control steps, chunk_index runs 0,1,… within a block,
video frame t is control step t, and
executed_actions[t] == decode(predicted_action_chunks[policy_step[t], chunk_index[t]])
where decode applies GR00T's gripper convention (g → 2g−1 → sign → −) to
dim 6 only. Every episode passed this check, and the check that
predicted_action_chunks == sampled_action_chunks[:, 0], at collection time.
manifest.csv
One row per episode: scene_variant, suite, task_id, task, prompt, episode, rollout_id, success, n_policy, n_control, control_hz, replan_steps, sim_failure_category, failing_predicate, labeled_captions, labeled_failure, labeler_model, vlm_failure_onset_frame, dir. The four label columns come from
the label pass (vlm_failure_onset_frame is blank for successes).
Labels
Each episode carries a Gemini gemini-3.5-flash (Batch API) description
of its video and, for failures, a localized failure onset. Produced offline from
rollout.mp4 + rollout.json only (no sim, no policy server). The exact prompt
templates are in gemini_prompts.md /
gemini_prompts.json, identical to
podolinsky/pi0.5-libero-10-features-v3,
and are also embedded verbatim in every labels.json under labeler.prompts.
499 of 500 episodes are labeled: the keyword pass returned no phrases for
one success (occluded/08_LIVING_ROOM_SCENE5_…/ep003), so it has no label
files. All 169 failures are labeled.
Two passes on one shared video session:
- Failure localizer (fork of Dan Lawson's
liberox-evals) — a coarse pass on the full video gives the failure mode (10-way taxonomy below), onset type (obvious_mistake/operator_intervention/timeout), onset time, and long + shortreason/recovery/preventionstrings; a second pass on a slowed ±window clip (1 clip-second = 1 rollout frame) refines the onset to a single frame (165 of 169 failures refined). Only runs on episodes the simulator scored as failures;{}for successes. - 3-second keyword phrases — a second turn on the same session captions
every consecutive 3 s window (
2–6word phrases), given the failure summary as context but told not to copy it in. 3–9 windows per episode.
Taxonomy (10 modes): wrong_object, wrong_target, press_failure,
open_close_failure, grasp_failure, object_displacement,
placement_or_insertion_failure, stuck_or_no_progress,
timeout_or_insufficient_progress, other. Definitions are in
gemini_prompts.md §1. v1 used a different 8-way set (a single
wrong_object_or_target, an unstable_or_dangerous_behavior mode, no press /
open-close modes), so mode counts are not directly comparable across versions (v1 was also labeled with a different
model, gemini-3.7-flash).
labels.json per episode: rollout_id / scene_variant / task_id /
task_file / instruction / success; labeler (backend, model,
refine, labeled_at, pipeline, prompts); semantic_timeline (list of
{segment_index, t_start_sec, t_end_sec, control_step_start/end, policy_step_start/end, phrase, description}); vlm_failure (mode, onset type /
seconds / timestamp / frame / step, coarse vs refined onset and window,
confidence, justification, reason / recovery / prevention long + summary, token
usage, vlm_raw_response; {} on success); failure_annotation (the onset
mapped onto the collection clocks: failure_control_step, failure_sim_step,
failure_policy_step, failure_chunk_index, first_post_failure_policy_step,
failure_type, correction_action).
labels.npz is the array-aligned copy: sem_t_start, sem_t_end,
sem_control_start, sem_control_end, sem_phrase, fail_onset_frame,
fail_onset_seconds, fail_mode, fail_reason, fail_reason_summary,
fail_recovery, fail_recovery_summary, fail_prevention,
fail_prevention_summary.
Failure statistics
| failure mode | n | % of failures | onset mean ± std (s) |
|---|---|---|---|
| stuck / no progress | 51 | 30.2% | 8.6 ± 3.7 |
| object displacement | 30 | 17.8% | 11.8 ± 6.7 |
| placement / insertion | 27 | 16.0% | 12.9 ± 7.0 |
| grasp failure | 22 | 13.0% | 8.6 ± 4.3 |
| open / close failure | 16 | 9.5% | 13.9 ± 3.1 |
| wrong target | 15 | 8.9% | 4.9 ± 4.4 |
| wrong object | 3 | 1.8% | 5.3 ± 3.1 |
| press failure | 3 | 1.8% | 10.7 ± 4.8 |
| timeout / insuff. progress | 2 | 1.2% | 25.9 ± 0.0 |
| other | 0 | 0.0% | – |
169 failures, all confidence: high. Onset type: obvious_mistake 118,
operator_intervention 49, timeout 2. Overall onset 10.2 ± 6.0 s
(median 8.7 s, range 0.6–26.0 s). other never occurs (0 episodes).
Per-failure reason / recovery / prevention summaries are in
FAILURE_ACTION_SUMMARIES.md.
Provenance
Collected with scripts/baselines/stac/collect_groot.py --with-features in the
12-Visual-Occlusion-Reasoning project, against a local GR00T policy server
(scripts/groot-libero-10/server/serve_groot_ws.py, GROOT_WITH_FEATURES=1,
embodiment LIBERO_PANDA). At each inference the server is queried K = 10
times on the same observation. Observations follow NVIDIA's LIBERO convention
(180°-rotated 256 px agentview + wrist images, 8-dim state);
num_steps_wait = 10, seed 7, num_inference_timesteps = 4 (flow matching).
Labels: the scripts/semantic_failure/ batch labeler (episode_batch.py,
Gemini Batch API); statistics:
scripts/semantic_failure/build_failure_stats.py.