assests / common /source /starBenchserver /benchmarks /simBenchmarks /DOMINO /environment /native_outcome.py
| """Native eval_policy post-action ordering, isolated for independent regression. | |
| All actors/poses/metrics here are private server state. This wrapper never | |
| creates an action, repeats a control, or calls an expert. | |
| """ | |
| from __future__ import annotations | |
| class NativeOutcome: | |
| def __init__(self, env, config, tracker_class): | |
| self.env = env | |
| self.success = self.finished = self.has_lifted = False | |
| self.fail_reason = None | |
| self.check_z_threshold = self.target_actor = self.start_z = None | |
| self.stop_on_contact = True | |
| dynamic = getattr(env, "get_dynamic_motion_config", lambda: None)() | |
| if dynamic and "check_z_threshold" in dynamic: | |
| self.check_z_threshold = dynamic["check_z_threshold"] | |
| self.target_actor = dynamic["check_z_actor"] if "check_z_actor" in dynamic else dynamic["target_actor"] | |
| self.start_z = self.target_actor.get_pose().p[2] | |
| if dynamic and "stop_on_contact" in dynamic: | |
| self.stop_on_contact = dynamic["stop_on_contact"] | |
| self.tracker = tracker_class(env, config) | |
| self.tracker.on_episode_start() | |
| env._metrics_tracker = self.tracker | |
| def after_action(self): | |
| """Exactly eval_policy's post-eval_func checks, in native order.""" | |
| env = self.env | |
| if self.check_z_threshold is not None and not self.has_lifted: | |
| self.has_lifted = self.target_actor.get_pose().p[2] - self.start_z > self.check_z_threshold | |
| if env.eval_success: | |
| self.success = self.check_z_threshold is None or self.has_lifted | |
| self.fail_reason = None if self.success else "object_not_lifted" | |
| self.finished = True | |
| return | |
| if env.use_dynamic: | |
| if env.check_gripper_contact_dynamic_object() and self.stop_on_contact: | |
| env.stop_dynamic_object_motion() | |
| if not getattr(env, "_dynamic_object_stopped", False) and not env.check_dynamic_object_boundary(): | |
| env.eval_fail = True | |
| self.fail_reason = "out_of_bounds" | |
| self.tracker.record_out_of_bounds() | |
| self.finished = True | |
| return | |
| if env.eval_fail: | |
| self.fail_reason = "eval_fail" | |
| self.finished = True | |
| return | |
| self.finished = env.take_action_cnt >= env.step_lim | |
| def metrics(self, seed): | |
| value = self.tracker.get_episode_metrics(self.success, self.fail_reason, seed=seed) | |
| return {"success": bool(value.success), "native_steps": int(value.steps_taken), | |
| "metrics_tracker_steps": int(value.steps_taken), | |
| "native_horizon": int(value.max_steps), "route_completion": float(value.route_completion), | |
| "manipulation_score": float(value.manipulation_score), | |
| "total_penalty_factor": float(value.total_penalty_factor), | |
| "fail_reason": value.fail_reason, "native_seed": seed, | |
| "penalty_events": [{"event_type": p.event_type, "timestep": int(p.timestep), | |
| "penalty_factor": float(p.penalty_factor), "details": p.details} | |
| for p in value.penalty_events]} | |