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Adds four validity metrics, computed from what --record_metric_inputs records:
Metric
Value
tracking_error
p50/p95 distance [mm] from each absolute end-effector target to the pose reached one step later
action_saturation
share of steps at which a clipped pose action sits at its bound
motion_smoothness
p50/p95 end-effector jerk [m/s³], labelled with the action noise generation added
joint_limit_ratio
share of episodes reaching 95% of a datasheet velocity or acceleration limit
The autodata entry also records the robot asset, whether each pose action is absolute, and the largest
action noise of the run.
A source baseline is evaluated with the run's entry, so it reads the run's pose-action layout and limits.
joint_limit_ratio needs datasheet hardware_limits in the embodiment YAML. The Franka configs have them;
GR1 and G1 report the metric as not applicable.
On a 50-demo Franka cube-stack run, 31 episodes reach 95% of a joint velocity limit with action noise and 16
without, against 1 of the 10 source demos. The report on 1,000 Franka episodes takes 29 s on one core.
Summary by CodeRabbit
New Features
Added metrics for absolute-pose tracking error, action saturation, motion smoothness, and joint-limit usage.
Pose-action metadata now distinguishes absolute targets from pose changes, and checks identify action ranges that exceed recorded data or configured limits.
Recorded metadata includes robot asset and joint details, simulator limits, and action-noise information.
Generation, annotation and retargeting now record what dataset metrics
need, without computing any metric:
- Every producer records step_success from the task's success term.
Generation also records the per-step EEF and commanded target poses,
but not object poses, which annotation keeps for the source pool.
- Each exported dataset gets an autodata entry in env_args: run id,
exported count, pose-action layout, robot joint names with simulator
limits, and optional datasheet hardware_limits from the embodiment.
- --result_file adds a run block (run id, seed, revision, input
digests, hardware, setup and generation time) and an attempt log, one
line per attempt with its failure cause and algorithm extras.
- GenerationAlgorithm.attempt_extras() lets an algorithm report
per-attempt facts; SkillGen reports its motion-planning time.
Metrics are plain functions registered with @Metric. Each declares the
evidence it requires (an HDF5 path, an env_args, run or attempt-log
field, or the source dataset); without it the report says which key is
missing instead of computing anything. A metric that raises is reported
as an error without hiding the others. The report refuses a dataset and
run record whose run ids or exported counts differ.
validate_dataset.py --report computes every registered metric, --metrics
loads more from a module, and --gate fails the command when a gated
metric is out of bounds, not applicable or at another version.
Built-in run metrics: generation success rate (stop-corrected, Wilson
interval), demos per GPU-hour, time to dataset, annotation pass rate,
and the failure-share and planning-time diagnostics.
- Each run writes its own attempt log, <stem>.<run_id>.attempts.jsonl,
so rerunning with the same --result_file keeps earlier logs, and an
interrupted rerun leaves the earlier result and its log consistent.
- A _failed dataset gets this run's autodata entry only when this run
kept failed episodes, so one left by an earlier run is not relabelled.
- The e2e helper checks the logged successes against the result.
- Docs: only generated datasets have a result file and attempt log.
Each run now writes its own attempt log. The report reads the log named
by the result when the result belongs to the dataset's run; otherwise it
sets the result aside with a warning and reads the log named by the
dataset's run id, so an interrupted rerun is still reported and runs are
never mixed.
…extras
- Metric recording is off by default, so datasets and result files
are unchanged unless --record_metric_inputs is given to generation,
annotation or retargeting. It records the per-step poses and task
success, the autodata entry and, with --result_file, the run block
and attempt log.
- GenerationResult no longer has failure_cause; SkillGen reports
planning_failed in its attempt extras, next to planning_time_s.
- metric_context() docstrings say what they record; the pose-action
entry's columns key is now action_slice.
- Docs: the run linkage holds once the run completes.
…un metrics tests in CI
- The report marks a metric whose value is not a finite number as an
error, and the gate counts a missing or non-numeric value as a
violation.
- validate_dataset.py errors when --result_file, --source, --metrics,
--gate or --report_file is given without --report.
- failure_share is replaced by planning_failure_rate, read from
SkillGen's planning_failed attempt extra.
- CI runs autodata_tests/metrics/.
- Docs: metrics need --record_metric_inputs at recording time.
…s term
- With --record_metric_inputs, the per-step pose term is added to a
custom --recorder_cfg too, unless it already records datagen info.
- Without a success term, step_success is not recorded instead of
failing on the first step.
- Episode checks run before any metric: finite values, aligned
per-step lengths, valid observed rotations, pose actions within
their clip bound, and camera streams (one frame per step, no black
frames, no frozen runs). An episode failing a check is counted in
correctness_pass_rate or image_stream_pass_rate and left out of every
dataset metric; the report lists the failures per check.
- final_success_rate: share of episodes whose task success holds at
their last step.
- action_rotation_error: how far the commanded target rotations are
from valid rotations.
- A metric declared with baseline=True is also computed on the source
dataset, using the run's autodata entry and control step.
- Docs: the metrics page lists the checks, generated from their
@check declarations.
…JSON
- The report refuses a result whose attempt and success counts differ
from its attempt log.
- Run and gate input checks raise ValueError, so they also hold under
python -O.
- Non-finite numbers in a metric's details are reported as null.
- A dataset whose env_args are not a JSON object reads as having none.
- An empty metrics mapping computes no metrics instead of all of them.
- Docs: how the report treats a result from another run; the example
metric gets its own name; joint-limit metrics are not mentioned yet.
- The end-to-end tests run the report through assert_ok_metrics.
…ble code
- No baseline metric reads the run's autodata entry or control step,
so the source dataset is evaluated as it is.
- One parser of the @Metric and @check declarations builds both docs
tables.
- Episode.datasets returns a list; the module constants sit together.
- When the result records the run's source, the report refuses a
--source with a different SHA-256, so source-based metrics read the
dataset the run started from.
- A gate entry whose min or max is not a finite number is an error
instead of a bound that can never be violated.
- A gate bound must be an int or float that is finite; YAML booleans
and mappings are errors instead of passing or failing the comparison.
- Docs: the source is verified only when the result file records it.
… from process start
- The console summary leads with a verdict (valid episodes, computed
and not-computed metrics, the gate), lists the computed metrics by
area with their source baseline and what their sample counts, and
groups the metrics that do not apply by what would make them apply.
With --gate, gated metrics are marked met or failed.
- @Metric declares what a metric's sample count counts.
- Pass rates report which checks ran and which did not apply;
valid_rotations no longer passes an episode that records no rotation.
- gate_results returns the result of every gated metric.
- Annotation records its mode and its setup time, timed from process
start like generation. time_to_dataset version 2 counts annotation
setup and keeps manual annotation time out of the total.
- Docs: how to read the summary; the generate and validate workflow
steps show how to record and report metrics.
… tests
- one_frame_per_step fails an episode that lacks a camera stream other
episodes record.
- finite reads floating-point arrays in chunks instead of whole.
- aligned_lengths fails an episode with no actions.
- valid_rotations checks every observed pose, all-zero ones included;
only commanded targets skip all-zero placeholders.
- A rotation's error includes |det R - 1|, so reflections fail.
- assert_ok_metrics asserts that every episode passes the checks.
…mmary
- Evidence records the group of every check considered; a check with no
failures entry did not apply, so skipped checks need no attribute.
- time_to_dataset reads the annotation mode and setup time with
defaults instead of branching.
- A failing check's line names its example episodes without an ellipsis.
- aligned_lengths skips scalar datasets, and valid_rotations reads the
last axis only of arrays that have one.
- action_rotation_error is not applicable to a target group without end
effectors.
- The console summary leaves out an interval with a missing bound, shows
every entry of a dict value without its primary entry, and a sample
count that is not an integer makes its metric an error.
- time_to_dataset counts annotation time only when the record says it
was automatic; other annotation time is reported apart.
… and gates
- A metric that returns something other than a Value, a nested dict,
details that are not a mapping, or a non-integer sample count is an
error row instead of breaking the report or its summary.
- Details and value keys are written as strings, so the report stays
valid JSON.
- env_args that are not JSON read as empty.
- load_gate validates the gate file before the report is built and
reads numbers YAML leaves as strings, such as 1e-3; a key on a value
without entries fails the gate instead of raising.
- Not-computed hints name the absent attempt log or source before the
algorithm, and a result file that does not exist is warned about.
- The report records the source dataset's checks, and the summary's
title says how many source episodes were valid.
- A rate just below one shows as >99.9% rather than 100%.
- A dataset or source whose env_args are not a JSON object, or whose
autodata entry lacks its run id or exported episode count, is refused
instead of read as having none, so a run is never matched to it on
episode count alone.
- validate_dataset.py prints a refused report as one error line, and
refuses a --source the validator cannot read.
- load_gate refuses unreadable YAML, repeated keys, unknown fields, a
key that is not an entry name and a version that is not an integer.
- A result file or attempt log that does not parse is named in the
error.
- The not-computed hint asks for a --source annotated with
--record_metric_inputs.
- autodata_metrics/records.py declares the shapes of the autodata entry,
the result file and its run block, and an attempt-log line. A record
is parsed once where it is read; a missing field, a wrong type, a
negative count or a number a float cannot hold is refused with an
error naming the file and field, and absent or null optional fields
are left out, so readers see a field well formed or not at all.
Fields no metric reads are not checked.
- Run files that cannot be read, are not UTF-8 or nest too deeply are
refused with the file named.
- Value checks itself when it is created, so a metric with a malformed
Value is an error row and the report reads only well-formed values.
- A source merged or edited after annotation has its annotation record
set aside, with a warning.
- The e2e helpers parse what generation and annotation write, so a
producer that drifts from the shapes fails its test.
- Generated tests mutate every field of every record and gate with
wrong values, and feed unreadable run files, asserting the report
refuses or reports them and never raises; a corpus of degenerate
episodes asserts no check raises.
- Docs: what the report trusts.
- tracking_error: distance from each absolute end-effector target to the
end effector one step later. Relative pose actions are not targets the
hand is meant to reach in one step, so they are skipped.
- action_saturation: share of steps at which a pose action sits at its
clip bound.
- motion_smoothness: end-effector jerk, labelled with the run's largest
action-noise scale.
- joint_limit_ratio: share of episodes that reach 95% of a datasheet
velocity or acceleration limit, with each peak ratio and worst joint.
The simulator clamps velocities at the limit, so counting only
episodes above it would read zero.
- Only end effectors that command a pose action are measured.
- With --record_metric_inputs, generation records its largest
action-noise scale (task action noise and motion-planner noise), the
entry records the robot asset its joints belong to, and each pose
action says whether it is an absolute target.
- A baseline reads the source with the run's entry filling in what the
source's own entry lacks, so shipped sources get the run's robot
layout and limits.
- Records: a dataset whose sim_args lack dt or decimation is still
reported, without the metrics that need them; an action slice must be
a non-empty range of non-negative indices.
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Inline comments:
Review comments at @autodata_metrics/evidence.py:
- Line 134: Update the merge logic around entry[key] so a present source
pose_action mapping, including an empty mapping, is preserved; use the run’s
pose_action mapping only when the source key is absent. Keep the existing merge
behavior for other keys unchanged.
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Walkthrough
The change records additional robot and pose-action metadata, updates metric evidence and baseline handling, and adds four validity metrics. Tests and documentation are updated to cover the metadata and metrics.
Changes: Metric data and evaluation
Layer / File(s)
Summary
Record pose and robot metric inputs autodata_interfaces/embodiments/*, autodata_interfaces/env/isaaclab_env_interface.py, autodata_metrics/records.py, scripts/generate_dataset.py, autodata_tests/metrics/test_records.py, autodata_tests/utils/test_validate_dataset.py, docs/pages/concepts/data_generator.rst
Pose-action metadata now records whether targets are absolute. Robot metadata includes the asset name, joint names, and simulator limits. Dataset metadata records the largest action-noise scale. Record parsing validates the added fields and non-empty action slices.
Expose metadata to metric evaluation autodata_metrics/evidence.py, autodata_metrics/report.py, autodata_metrics/viability.py, autodata_tests/metrics/test_viability.py
Evidence.entry provides access to autodata metadata. Baseline evaluation adapts source evidence with the run’s metadata. Existing viability metrics read metadata through the evidence property.
Calculate and validate validity metrics autodata_metrics/validity.py, autodata_tests/metrics/test_validity.py, autodata_tests/e2e/*, docs/pages/references/metrics.rst
Validity evaluation adds tracking error, action saturation, motion smoothness, and joint-limit ratios. Tests cover the metric calculations, and end-to-end tests include the new metrics.
sequenceDiagram
participant Evidence
participant ValidityMetrics
participant EpisodeData
Evidence->>ValidityMetrics: provide entry metadata
ValidityMetrics->>EpisodeData: read actions, poses, and joint velocities
EpisodeData-->>ValidityMetrics: return recorded arrays
ValidityMetrics-->>Evidence: produce metric values and details
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Possibly related PRs
isaac-sim/AutoData#84: Records pose actions, targets, joint metadata, and simulator limits that this change extends for metric evaluation.
isaac-sim/AutoData#91: Provides the validity and evidence framework that this change extends with additional metrics and baseline adaptation.
isaac-sim/AutoData#90: Introduces the offline metrics and report framework used by the new checks.
Merge Risk: Low · up to 722c7
Preserve explicit empty pose-action mappings before merging so baseline metric comparisons remain accurate.
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- no_frozen_frames: a camera counts as frozen when it repeats one frame
for 1 s of recorded time, converted with the dataset's dt and
decimation, so the threshold no longer depends on the control rate.
A dataset that records no control step skips the check.
- aligned_lengths: subtask signals must hold one entry per action. Each
signal is counted by its entries, since sources store it as a
(steps,) column or a (1, steps) row.
- valid_rotations: an observed pose not shaped (steps, 4, 4), or a root
pose not shaped (steps, 7), fails the episode instead of being
skipped.
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Actionable comments posted: 2
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @autodata_metrics/evidence.py:
- Line 131: Update the baseline.env_args merge so hardware_limits.velocity and
hardware_limits.acceleration are merged independently from run.entry and
self.entry, preserving run-provided limit types missing from self.entry and
letting self.entry values take precedence when both define the same type.
Review comments at @autodata_metrics/validity.py:
- Line 295: Validate each declared action_slice against the episode’s action
width before computing saturation, rejecting slices that extend beyond the
action vector. Apply the same bounds check in parse_env_args and
pose_action_bounds so malformed layouts cannot pass validity checks.
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…actions
- A source baseline keeps its own datasheet limits and takes the run's for
the kinds it lacks, so a source with only velocity limits still gets an
acceleration baseline.
- pose_action_bounds fails an episode whose declared pose-action slice
runs past its action columns, instead of reading an empty slice as never
saturated.
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🪄 Fix comments: Fix CodeRabbit comments on this PR
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @autodata_metrics/evidence.py:
- Line 131: Update the merge in the `entry` assignment so `joints` fields are
merged separately, with source values taking precedence while retaining the
run’s `joints.asset` when the source omits it. Preserve the existing top-level
merge behavior for all other fields.
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A source entry that records joint names without the robot asset kept no
asset in its baseline, so joint_limit_ratio had no baseline. Each nested
record, such as joints and hardware_limits, now takes the run's fields
where the source's lacks them.
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🪄 Fix comments: Fix CodeRabbit comments on this PR
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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @autodata_metrics/evidence.py:
- Line 134: Update the merge logic around entry[key] so a present source
pose_action mapping, including an empty mapping, is preserved; use the run’s
pose_action mapping only when the source key is absent. Keep the existing merge
behavior for other keys unchanged.
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Minor · Functional Correctness · Quick win
Preserve the source’s pose-action channels.
If the source has pose_action: {}, this merge adds the run’s channels even though the source records no pose actions. Baseline metrics can then treat tracking-only channels or passthrough actions as commanded poses. The adapter explicitly excludes tracking-only channels from pose_action. (raw.githubusercontent.com)
Keep the source’s pose_action mapping when it is present. Use the run’s mapping only when it is absent.
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Review comment at @autodata_metrics/evidence.py at line 134:
Update the merge logic around entry[key] so a present source pose_action
mapping, including an empty mapping, is preserved; use the run’s pose_action
mapping only when the source key is absent. Keep the existing merge behavior for
other keys unchanged.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
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Stacked on #91; review that first.
Adds four validity metrics, computed from what
--record_metric_inputsrecords:tracking_erroraction_saturationmotion_smoothnessjoint_limit_ratioautodataentry also records the robot asset, whether each pose action is absolute, and the largestaction noise of the run.
joint_limit_rationeeds datasheethardware_limitsin the embodiment YAML. The Franka configs have them;GR1 and G1 report the metric as not applicable.
On a 50-demo Franka cube-stack run, 31 episodes reach 95% of a joint velocity limit with action noise and 16
without, against 1 of the 10 source demos. The report on 1,000 Franka episodes takes 29 s on one core.
Summary by CodeRabbit