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chore(unitree_g1): silence bandit B105 on SONIC token prefixes

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The motion_token / motion_token_state constants are feature-key prefixes, not
secrets; mark them #nosec so the pre-commit bandit hook passes.

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Martino Russi
2026-07-30 12:03:37 +02:00
parent 0a9b9d9a93
commit 9f32f57b59
1 changed files with 97 additions and 11 deletions
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src/lerobot/robots/unitree_g1/controllers/sonic_whole_body.py
+97 -11
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@@ -57,11 +57,35 @@ TOKEN_DIM = 64 # decoder latent size
# added on top of this. # added on top of this.
DEFAULT_ANGLES = np.array( DEFAULT_ANGLES = np.array(
[ [
-0.312, 0.0, 0.0, 0.669, -0.363, 0.0, -0.312,
-0.312, 0.0, 0.0, 0.669, -0.363, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0,
0.2, 0.2, 0.0, 0.6, 0.0, 0.0, 0.0, 0.669,
0.2, -0.2, 0.0, 0.6, 0.0, 0.0, 0.0, -0.363,
0.0,
-0.312,
0.0,
0.0,
0.669,
-0.363,
0.0,
0.0,
0.0,
0.0,
0.2,
0.2,
0.0,
0.6,
0.0,
0.0,
0.0,
0.2,
-0.2,
0.0,
0.6,
0.0,
0.0,
0.0,
], ],
dtype=np.float32, dtype=np.float32,
) )
@@ -110,10 +134,10 @@ def compute_kp_kd():
# Action-feature prefix for the latent-token interface (see _extract_token_from_action). # Action-feature prefix for the latent-token interface (see _extract_token_from_action).
TOKEN_ACTION_PREFIX = "motion_token" TOKEN_ACTION_PREFIX = "motion_token" # nosec B105 - feature-key prefix, not a secret
# Proprio-state prefix for the token interface: the robot echoes the last commanded token # Proprio-state prefix for the token interface: the robot echoes the last commanded token
# here so ``lerobot-rollout`` aggregates it into a 64-D ``observation.state``. # here so ``lerobot-rollout`` aggregates it into a 64-D ``observation.state``.
TOKEN_STATE_PREFIX = "motion_token_state" TOKEN_STATE_PREFIX = "motion_token_state" # nosec B105 - feature-key prefix, not a secret
def token_action_key(i: int) -> str: def token_action_key(i: int) -> str:
@@ -142,10 +166,72 @@ INIT_RAMP_S = 3.0
# all-zero token, which is off the learned manifold and decodes to a slightly goofy stance. # all-zero token, which is off the learned manifold and decodes to a slightly goofy stance.
# This one decodes to a stable, natural standing pose. # This one decodes to a stable, natural standing pose.
_NEUTRAL_TOKEN_CODES = np.array( _NEUTRAL_TOKEN_CODES = np.array(
[-1, 3, 1, -1, 1, -3, 6, 1, 1, 1, -2, -4, -2, 0, -3, -1, [
2, -1, -3, -5, 3, 1, 1, -4, -1, -1, 1, -7, 0, 1, 2, -2, -1,
5, -2, -2, -4, 0, -1, 3, -1, 0, -5, -1, 0, -4, 0, 0, -1, 3,
-1, 2, -2, 1, 3, 3, 1, 0, 0, 6, 0, -7, 3, 0, 2, -2], 1,
-1,
1,
-3,
6,
1,
1,
1,
-2,
-4,
-2,
0,
-3,
-1,
2,
-1,
-3,
-5,
3,
1,
1,
-4,
-1,
-1,
1,
-7,
0,
1,
2,
-2,
5,
-2,
-2,
-4,
0,
-1,
3,
-1,
0,
-5,
-1,
0,
-4,
0,
0,
-1,
-1,
2,
-2,
1,
3,
3,
1,
0,
0,
6,
0,
-7,
3,
0,
2,
-2,
],
dtype=np.float32, dtype=np.float32,
) )
NEUTRAL_TOKEN = _NEUTRAL_TOKEN_CODES / 16.0 # FSQ Div(16): integer codes -> on-grid token NEUTRAL_TOKEN = _NEUTRAL_TOKEN_CODES / 16.0 # FSQ Div(16): integer codes -> on-grid token