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Datasets:
witcheer
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microduck-hermes-runs

Size:
n<1K
Tags:
robotics
hermes-agent
agents
tool-use
microduck
pollen-robotics
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
wall: double
sim_time: double
trunk: list<item: double>
  child 0, item: double
quat: list<item: double>
  child 0, item: double
positions: list<item: double>
  child 0, item: double
tool_calls: list<item: string>
  child 0, item: string
robot_odometry_walk_m: double
sentence: string
session_seconds_first_to_last_message: double
simulator: string
client_version: string
agent: string
files: struct<transcript.json: string, transcript.md: string, state.jsonl: string, monitor.log: string, rep (... 16 chars omitted)
  child 0, transcript.json: string
  child 1, transcript.md: string
  child 2, state.jsonl: string
  child 3, monitor.log: string
  child 4, replay.mp4: string
note: string
skill: string
label: string
start_posture: string
outcome: string
sim_truth_walk_m: double
model: string
model_state: string
client: string
to
{'sentence': Value('string'), 'agent': Value('string'), 'model': Value('string'), 'skill': Value('string'), 'client': Value('string'), 'simulator': Value('string'), 'label': Value('string'), 'start_posture': Value('string'), 'model_state': Value('string'), 'client_version': Value('string'), 'tool_calls': List(Value('string')), 'session_seconds_first_to_last_message': Value('float64'), 'sim_truth_walk_m': Value('float64'), 'robot_odometry_walk_m': Value('float64'), 'outcome': Value('string'), 'note': Value('string'), 'files': {'transcript.json': Value('string'), 'transcript.md': Value('string'), 'state.jsonl': Value('string'), 'monitor.log': Value('string'), 'replay.mp4': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              wall: double
              sim_time: double
              trunk: list<item: double>
                child 0, item: double
              quat: list<item: double>
                child 0, item: double
              positions: list<item: double>
                child 0, item: double
              tool_calls: list<item: string>
                child 0, item: string
              robot_odometry_walk_m: double
              sentence: string
              session_seconds_first_to_last_message: double
              simulator: string
              client_version: string
              agent: string
              files: struct<transcript.json: string, transcript.md: string, state.jsonl: string, monitor.log: string, rep (... 16 chars omitted)
                child 0, transcript.json: string
                child 1, transcript.md: string
                child 2, state.jsonl: string
                child 3, monitor.log: string
                child 4, replay.mp4: string
              note: string
              skill: string
              label: string
              start_posture: string
              outcome: string
              sim_truth_walk_m: double
              model: string
              model_state: string
              client: string
              to
              {'sentence': Value('string'), 'agent': Value('string'), 'model': Value('string'), 'skill': Value('string'), 'client': Value('string'), 'simulator': Value('string'), 'label': Value('string'), 'start_posture': Value('string'), 'model_state': Value('string'), 'client_version': Value('string'), 'tool_calls': List(Value('string')), 'session_seconds_first_to_last_message': Value('float64'), 'sim_truth_walk_m': Value('float64'), 'robot_odometry_walk_m': Value('float64'), 'outcome': Value('string'), 'note': Value('string'), 'files': {'transcript.json': Value('string'), 'transcript.md': Value('string'), 'state.jsonl': Value('string'), 'monitor.log': Value('string'), 'replay.mp4': Value('string')}}
              because column names don't match

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Microduck driven by Hermes Agent: simulation runs

One sentence typed to Hermes Agent, and a Pollen Robotics Microduck does it. Hermes Agent picks and runs every command itself. This dataset logs every such run: the sentence, the agent's full transcript, the simulator's ground truth, the robot daemon's own monitor, and a replay video.

Simulation only. Not yet tested on a real Microduck. The simulator runs Pollen's real robot daemon (robotd), so the commands, the safety layer and the control loop are the ones the physical robot uses. Only the body is simulated.

Sibling dataset: witcheer/microduck-skill-tree (the RL policies we train for the same robot). Collection: microduck skill tree (sim only).

How a run works

  • Hermes Agent runs on the same Linux box as the simulator, on a local model (Qwen3.8-27B Q6_K through llama.cpp, one RTX 5090). No cloud model.
  • It gets one skill, tools/microduck-sim-SKILL.md, and the terminal tool. The skill tells it about one command-line client, tools/duckctl.py, with seven commands: health, status, stand, sit, walk, look, stop.
  • The client only sends intents to robotd over its unix socket (JSON-RPC 2.0). It never touches a motor. robotd owns the motors and the safety layer, and zeroes the velocity if walk commands stop for 500 ms.
  • robotd does not limit velocity itself, so the client refuses anything outside the ranges the walking policy was trained on (vx ±0.4 m/s, vy ±0.3 m/s, yaw ±1.0 rad/s).
  • Ground truth comes from the simulator: tools/duck-state-rec.py polls the MuJoCo body server's read-only read op at 30 Hz. tools/duck-replay-render.py replays that state offscreen, so every video is the simulator's exact state, not a re-simulation.
  • The client is ours. Pollen ships a different app also called duckctl, so treat this name as local to these runs.

Runs

run start posture tool calls, in order walked (sim truth / robot odometry) time outcome
2026-09-24 walk-then-sit take 1 standing health, walk, sit, status 0.582 m / 0.555 m 29.1 s (cold model) pass
2026-09-24 walk-then-sit take 2 sitting health, walk, stand, walk, sit, status 0.577 m / 0.545 m 32.8 s pass after self-correction
2026-09-24 walk-then-sit take 3 sitting health, stand, walk, sit, status 0.578 m / 0.547 m 21.6 s pass

The sentence was "make the duck walk forward for five seconds then sit" in all three. Time runs from the first to the last message in the session, and take 3 took 24.4 s wall clock from the sentence to the answer. Walk is --vx 0.3 --seconds 5 every time.

What the runs taught us:

  • Take 1: the final answer quoted the robot's odometry x (measured since the daemon started) as "distance from start". The skill now says to report deltas only.
  • Take 2: health printed robot.mode = walk, which is the locomotion hardware mode, not posture. The model read it as standing and sent a walk to a seated duck. The walk output showed an odometry delta of 0.0 and policy sit, so it stood the duck up and walked again. After this run, health prints posture (the policy label) and hardware_mode.
  • Take 3: after that fix, the model went straight to stand from the seated start. This is the take in the X post.

What is in a run folder

  • run.json: sentence, agent and model, client version, start posture, the tool-call sequence, distances, outcome, and a note on what the run showed.
  • transcript.json / transcript.md: the full Hermes Agent session, with the user sentence, the model's reasoning, every tool call with its arguments, and every tool output. The system prompt is removed; everything else is verbatim.
  • state.jsonl: simulator ground truth at 30 Hz (wall time, sim time, trunk position, trunk quaternion, 14 joint positions in robotd wire order).
  • monitor.log: robotd's own monitor at 2 Hz (requested vs applied velocity, active policy, deadman).
  • replay.mp4: an offscreen MuJoCo replay of state.jsonl, fixed camera, real time.

Reproduce

Pollen's microduck repo at 6507d2e (scripts/duck-sim up) plus Hermes Agent with the skill installed under ~/.hermes/skills/robotics/microduck-sim/. tools/duck-p1-exit.sh runs one sentence with the recorder and monitor attached, and tools/hermes-run-export.py exports the session. The scripts carry /home/witcheer paths from the box they ran on; change them for yours.

Limits

  • Simulation only. The real robot is not here yet. When it is, the same client points at it over ssh (--ssh-host), and those runs will be labelled separately.
  • One sentence and three takes so far. This is a run log, not a benchmark.
  • Local model output varies between runs. The transcripts are what happened, not a best-of selection: every run of this sentence is here.
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