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406662d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from isaaclab.app import AppLauncher
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import pytest
import torch
from isaaclab.managers import TerminationManager, TerminationTermCfg
from isaaclab.sim import SimulationContext
class DummyEnv:
"""Minimal mutable env stub for the termination manager tests."""
def __init__(self, num_envs: int, device: str, sim: SimulationContext):
self.num_envs = num_envs
self.device = device
self.sim = sim
self.counter = 0 # mutable step counter used by test terms
def fail_every_5_steps(env) -> torch.Tensor:
"""Returns True for all envs when counter is a positive multiple of 5."""
cond = env.counter > 0 and (env.counter % 5 == 0)
return torch.full((env.num_envs,), cond, dtype=torch.bool, device=env.device)
def fail_every_10_steps(env) -> torch.Tensor:
"""Returns True for all envs when counter is a positive multiple of 10."""
cond = env.counter > 0 and (env.counter % 10 == 0)
return torch.full((env.num_envs,), cond, dtype=torch.bool, device=env.device)
def fail_every_3_steps(env) -> torch.Tensor:
"""Returns True for all envs when counter is a positive multiple of 3."""
cond = env.counter > 0 and (env.counter % 3 == 0)
return torch.full((env.num_envs,), cond, dtype=torch.bool, device=env.device)
@pytest.fixture
def env():
sim = SimulationContext()
return DummyEnv(num_envs=20, device="cpu", sim=sim)
def test_initial_state_and_shapes(env):
cfg = {
"term_5": TerminationTermCfg(func=fail_every_5_steps),
"term_10": TerminationTermCfg(func=fail_every_10_steps),
}
tm = TerminationManager(cfg, env)
# Active term names
assert tm.active_terms == ["term_5", "term_10"]
# Internal buffers have expected shapes and start as all False
assert tm._term_dones.shape == (env.num_envs, 2)
assert tm._last_episode_dones.shape == (env.num_envs, 2)
assert tm.dones.shape == (env.num_envs,)
assert tm.time_outs.shape == (env.num_envs,)
assert tm.terminated.shape == (env.num_envs,)
assert torch.all(~tm._term_dones) and torch.all(~tm._last_episode_dones)
def test_term_transitions_and_persistence(env):
"""Concise transitions: single fire, persist, switch, both, persist.
Uses 3-step and 5-step terms and verifies current-step values and last-episode persistence.
"""
cfg = {
"term_3": TerminationTermCfg(func=fail_every_3_steps, time_out=False),
"term_5": TerminationTermCfg(func=fail_every_5_steps, time_out=False),
}
tm = TerminationManager(cfg, env)
# step 3: only term_3 -> last_episode [True, False]
env.counter = 3
out = tm.compute()
assert torch.all(tm.get_term("term_3")) and torch.all(~tm.get_term("term_5"))
assert torch.all(out)
assert torch.all(tm._last_episode_dones[:, 0]) and torch.all(~tm._last_episode_dones[:, 1])
# step 4: none -> last_episode persists [True, False]
env.counter = 4
out = tm.compute()
assert torch.all(~out)
assert torch.all(~tm.get_term("term_3")) and torch.all(~tm.get_term("term_5"))
assert torch.all(tm._last_episode_dones[:, 0]) and torch.all(~tm._last_episode_dones[:, 1])
# step 5: only term_5 -> last_episode [False, True]
env.counter = 5
out = tm.compute()
assert torch.all(~tm.get_term("term_3")) and torch.all(tm.get_term("term_5"))
assert torch.all(out)
assert torch.all(~tm._last_episode_dones[:, 0]) and torch.all(tm._last_episode_dones[:, 1])
# step 15: both -> last_episode [True, True]
env.counter = 15
out = tm.compute()
assert torch.all(tm.get_term("term_3")) and torch.all(tm.get_term("term_5"))
assert torch.all(out)
assert torch.all(tm._last_episode_dones[:, 0]) and torch.all(tm._last_episode_dones[:, 1])
# step 16: none -> persist [True, True]
env.counter = 16
out = tm.compute()
assert torch.all(~out)
assert torch.all(~tm.get_term("term_3")) and torch.all(~tm.get_term("term_5"))
assert torch.all(tm._last_episode_dones[:, 0]) and torch.all(tm._last_episode_dones[:, 1])
def test_time_out_vs_terminated_split(env):
cfg = {
"term_5": TerminationTermCfg(func=fail_every_5_steps, time_out=False), # terminated
"term_10": TerminationTermCfg(func=fail_every_10_steps, time_out=True), # timeout
}
tm = TerminationManager(cfg, env)
# Step 5: terminated fires, not timeout
env.counter = 5
out = tm.compute()
assert torch.all(out)
assert torch.all(tm.terminated) and torch.all(~tm.time_outs)
# Step 10: both fire; timeout and terminated both True
env.counter = 10
out = tm.compute()
assert torch.all(out)
assert torch.all(tm.terminated) and torch.all(tm.time_outs)
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