microduck_rl/tests/test_head_pose_bias.py
Upstream Snapshot 47372443ff Import upstream snapshot d424a0c899f6b33cbd3daeb279913134349c0b63
Upstream: https://github.com/pollen-robotics/microduck_rl
Upstream-Commit: d424a0c899f6b33cbd3daeb279913134349c0b63
Upstream-Branch: develop
2026-08-28 15:41:56 +08:00

177 lines
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Python

"""head_pose_bias_penalty: prices sustained standing droop, never the recovery.
The velocity-env lesson (run 5yay13u4): instantaneous posture precision is an
unescapable tax on motion. The standup lesson (retired head_impact_penalty):
any head cost active during the ground phase blocks the head-pivot flip. So
this term must (a) charge only the DC bias, (b) accumulate NOTHING while
fallen, and (c) start the clock from ~zero on arrival upright.
"""
import math
import torch
from mjlab_microduck.tasks import mdp as microduck_mdp
class _Data:
def __init__(self, n):
self.joint_pos = torch.zeros(n, 6)
self.default_joint_pos = torch.zeros(n, 6)
self.root_link_pos_w = torch.zeros(n, 3)
self.root_link_quat_w = torch.zeros(n, 4)
self.root_link_quat_w[:, 0] = 1.0 # upright
class _Asset:
def __init__(self, data):
self.data = data
class _Terrain:
def __init__(self, n):
self.env_origins = torch.zeros(n, 3)
class _Scene:
def __init__(self, asset, n):
self._asset = asset
self.terrain = _Terrain(n)
def __getitem__(self, _):
return self._asset
class _Cmd:
def __init__(self, n, dim=4):
self.cmd = torch.zeros(n, dim)
def get_command(self, _):
return self.cmd
class _Env:
def __init__(self, n):
self.num_envs = n
self.device = "cpu"
self.step_dt = 0.02
self.episode_length_buf = torch.full((n,), 10, dtype=torch.long)
self.scene = _Scene(_Asset(_Data(n)), n)
self.command_manager = _Cmd(n)
# Pre-seed the neck-id cache (normally built from the real asset).
self._head_pose_neck_ids = torch.tensor([0, 1, 2, 3])
self._head_pose_bl_ids = torch.tensor([0, 0, 0, 0])
self._head_pose_bl_mask = torch.zeros(4)
GATE = dict(
gate_height_low=0.09, gate_height_high=0.11,
gate_tilt_full_deg=20.0, gate_tilt_zero_deg=45.0,
)
def _set_pose(env, z, pitch_deg):
env.scene._asset.data.root_link_pos_w[:, 2] = z
half = math.radians(pitch_deg) / 2
q = torch.tensor([math.cos(half), 0.0, math.sin(half), 0.0])
env.scene._asset.data.root_link_quat_w[:] = q
def _run(env, steps, **kw):
out = None
for _ in range(steps):
out = microduck_mdp.head_pose_bias_penalty(env, tau_s=1.0, **kw)
return out
def test_prone_accumulates_nothing():
env = _Env(2)
_set_pose(env, z=0.05, pitch_deg=90.0) # face-down on the ground
env.scene._asset.data.joint_pos[:, :4] = 0.5 # huge head "error" (28°)
out = _run(env, 200, **GATE) # 4 s of prone thrash
assert torch.allclose(out, torch.zeros(2), atol=1e-9), out
def test_arrival_starts_from_zero_then_charges_true_bias():
env = _Env(2)
_set_pose(env, z=0.05, pitch_deg=90.0)
env.scene._asset.data.joint_pos[:, :4] = 0.5
_run(env, 200, **GATE) # ground phase: EMA stays 0
_set_pose(env, z=0.117, pitch_deg=0.0) # recovery completes
env.scene._asset.data.joint_pos[:, :4] = math.radians(15) # 15° droop
first = _run(env, 1, **GATE)
assert first.abs().max() < 0.01, f"finish-line wall: {first}" # no arrival spike
settled = _run(env, 300, **GATE) # 6 s standing
assert abs(-settled[0].item() - math.radians(15)) < 0.01 # charges the droop
def test_fall_stops_the_charge_immediately():
env = _Env(2)
_set_pose(env, z=0.117, pitch_deg=0.0)
env.scene._asset.data.joint_pos[:, :4] = math.radians(15)
_run(env, 300, **GATE)
_set_pose(env, z=0.05, pitch_deg=90.0) # falls
out = _run(env, 1, **GATE)
assert torch.allclose(out, torch.zeros(2), atol=1e-9), out
def test_ungated_matches_velocity_env_behavior():
# No gate params -> plain EMA of the raw error (the velocity-env term).
env = _Env(2)
_set_pose(env, z=0.05, pitch_deg=90.0) # pose must be irrelevant
env.scene._asset.data.joint_pos[:, :4] = math.radians(15)
out = _run(env, 300)
assert abs(-out[0].item() - math.radians(15)) < 0.01
def test_reset_clears_the_ema():
env = _Env(2)
_set_pose(env, z=0.117, pitch_deg=0.0)
env.scene._asset.data.joint_pos[:, :4] = math.radians(15)
_run(env, 300, **GATE)
env.episode_length_buf[0] = 1 # env 0 just reset
out = _run(env, 1, **GATE)
# One step after reset the EMA holds exactly alpha*err (~0.005), while the
# non-reset env still carries the full settled bias (~0.26).
assert out[0].abs() < 0.01 and out[1].abs() > 0.2
def test_standup_cfg_wiring():
from mjlab_microduck.tasks.microduck_standup_env_cfg import (
make_microduck_standup_env_cfg,
)
cfg = make_microduck_standup_env_cfg()
term = cfg.rewards["head_pose_bias"]
assert term.weight == 0.0 # discovery phase untouched
assert term.params["gate_height_low"] is not None
# Gate values identical to arrival_damping so "standing" means one thing.
ad = cfg.rewards["arrival_damping"].params
assert term.params["gate_height_low"] == ad["height_low"]
assert term.params["gate_tilt_full_deg"] == ad["tilt_full_deg"]
stages = cfg.curriculum["head_pose_bias_weight"].params["weight_stages"]
assert stages[0]["weight"] == 0.0
assert min(s["step"] for s in stages if s["weight"] > 0) >= 3000 * 24
def test_velocity_cfg_unchanged_no_gate():
from mjlab_microduck.tasks.microduck_velocity_env_cfg import (
make_microduck_velocity_env_cfg,
)
cfg = make_microduck_velocity_env_cfg()
assert "gate_height_low" not in cfg.rewards["head_pose_bias"].params
def test_velstand_inherited_term_is_gated():
# Velstand episodes survive falls — the inherited velocity-env term must not
# charge the ground phase.
from mjlab_microduck.tasks.microduck_velstand_env_cfg import (
make_microduck_velstand_env_cfg,
)
cfg = make_microduck_velstand_env_cfg()
params = cfg.rewards["head_pose_bias"].params
assert params.get("gate_height_low") is not None
assert params["gate_tilt_zero_deg"] == 40.0 # REWARD_GATE_TILT_DEG