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"""
Tiffin Packing Environment — OpenEnv Server Implementation.
Wraps the packing simulation into the OpenEnv Environment base class,
exposing step(), reset(), and state() for LLM agent interaction.
"""
from __future__ import annotations
from typing import Any, Optional
from uuid import uuid4
try:
from openenv.core.env_server import Environment
except ImportError:
# Fallback for local testing without openenv installed
class Environment:
def __init__(self, **kwargs): pass
def reset(self, **kwargs): raise NotImplementedError
def step(self, action, **kwargs): raise NotImplementedError
@property
def state(self): raise NotImplementedError
from tiffin_packer.models import TiffinAction, TiffinObservation, TiffinState
from tiffin_packer.simulation.engine import PackingSimulation
from tiffin_packer.vlm.classifier import FoodClassifier
from tiffin_packer.tasks import get_task_config, list_tasks
from tiffin_packer.grader import grade, grade_detailed
class TiffinPackingEnvironment(Environment):
"""
OpenEnv-compliant tiffin packing environment.
An LLM agent controls a robotic arm to identify food items using VLM
and pack them into the correct tiffin containers under real-world
constraints (type compatibility, volume, temperature, fragility).
Supports 3 tasks: easy, medium, hard.
"""
_instance = None
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if getattr(self, "_initialized", False):
return
super().__init__()
self.sim = PackingSimulation()
self.vlm = FoodClassifier()
self._state = TiffinState()
self._identified_items: set = set()
self._task_config = None
self._initialized = True
def reset(
self,
seed: Optional[int] = None,
episode_id: Optional[str] = None,
**kwargs: Any,
) -> TiffinObservation:
"""
Reset the environment for a new episode.
Args:
seed: Optional random seed for reproducibility.
episode_id: Optional custom episode ID.
**kwargs: Must include 'task_id' (easy/medium/hard).
Returns:
Initial TiffinObservation with scene description.
"""
task_id = kwargs.get("task_id", "easy")
# Load task configuration
self._task_config = get_task_config(task_id, seed=seed)
# Reset simulation
self.sim.reset(
food_items=self._task_config.food_items,
containers=self._task_config.containers,
seed=seed,
)
# Reset state
self._state = TiffinState(
episode_id=episode_id or str(uuid4()),
step_count=0,
task_id=task_id,
items_packed=0,
total_items=len(self._task_config.food_items),
items_identified=0,
packing_log=[],
constraints_violated=[],
)
self._identified_items = set()
# Build initial observation
return self._build_observation(
reward=0.0,
done=False,
feedback=(
f"Episode started! Task: {task_id.upper()}\n\n"
f"{self._task_config.description}\n\n"
f"You have {self._task_config.max_steps} steps to pack "
f"{len(self._task_config.food_items)} food items into "
f"{len(self._task_config.containers)} containers.\n\n"
f"Start by using 'observe' to see the scene, then 'identify' "
f"each food item before packing."
),
)
def step(
self,
action: TiffinAction,
timeout_s: Optional[float] = None,
**kwargs: Any,
) -> TiffinObservation:
"""
Execute one step in the environment.
Args:
action: TiffinAction with command and optional target_id.
timeout_s: Optional timeout (unused).
Returns:
TiffinObservation with updated scene state.
"""
self._state.step_count += 1
reward = 0.0
done = False
vlm_result = None
feedback = ""
command = action.command.lower().strip()
target_id = action.target_id
# --- Dispatch command ---
if command == "observe":
_, feedback, reward = self.sim.observe()
elif command == "identify":
if target_id is None:
feedback = "Error: 'identify' requires a target_id (food item ID)."
reward = -0.1
else:
success, feedback, reward, vlm_result = self.sim.identify(target_id)
if success and vlm_result and vlm_result.get("name"):
self._identified_items.add(target_id)
self._state.items_identified = len(self._identified_items)
elif command == "pick":
if target_id is None:
feedback = "Error: 'pick' requires a target_id (food item ID)."
reward = -0.1
else:
success, feedback, reward = self.sim.pick(target_id)
elif command == "place":
if target_id is None:
feedback = "Error: 'place' requires a target_id (container ID)."
reward = -0.1
else:
success, feedback, reward = self.sim.place(target_id)
if success:
self._state.items_packed = sum(
1
for i in self.sim.food_items
if i.status == "packed"
)
self._state.packing_log = list(self.sim.packing_log)
elif command == "pour":
if target_id is None:
feedback = "Error: 'pour' requires a target_id (container ID)."
reward = -0.1
else:
success, feedback, reward = self.sim.pour(target_id)
if success:
self._state.items_packed = sum(
1
for i in self.sim.food_items
if i.status == "packed"
)
self._state.packing_log = list(self.sim.packing_log)
else:
feedback = (
f"Unknown command: '{command}'. "
f"Available commands: {self.sim.get_available_commands()}"
)
reward = -0.1
# --- Time penalty ---
reward -= 0.02
# --- Check termination ---
done = (
self.sim.all_packed
or self._state.step_count >= self._task_config.max_steps
)
# --- Final grading ---
final_score = None
grade_breakdown = None
if done:
grade_breakdown = grade_detailed(
self._state.packing_log, self._task_config
)
final_score = grade_breakdown["final_score"]
reward += final_score # bonus = final grade
if self.sim.all_packed:
feedback += f"\n\n🎉 All items packed! Final score: {final_score:.4f}"
else:
feedback += (
f"\n\n⏰ Time's up! {self.sim.unpacked_count} items remaining. "
f"Final score: {final_score:.4f}"
)
return self._build_observation(
reward=reward,
done=done,
feedback=feedback,
vlm_result=vlm_result,
final_score=final_score,
grade_breakdown=grade_breakdown,
)
@property
def state(self) -> TiffinState:
"""Return the current episode state."""
return self._state
# -------------------------------------------------------------------
# Helpers
# -------------------------------------------------------------------
def _build_observation(
self,
reward: float = 0.0,
done: bool = False,
feedback: str = "",
vlm_result: dict = None,
final_score: float = None,
grade_breakdown: dict = None,
) -> TiffinObservation:
"""Build a TiffinObservation from current state."""
metadata = {}
if final_score is not None:
metadata["final_score"] = final_score
if grade_breakdown is not None:
metadata["grade_breakdown"] = grade_breakdown
return TiffinObservation(
done=done,
reward=round(reward, 4),
metadata=metadata,
scene_description=self.sim.get_scene_description(),
food_items=[
item.to_dict(hide_unidentified=True)
for item in self.sim.food_items
],
containers=[c.to_dict() for c in self.sim.containers],
held_item=(
self.sim.held_item.to_dict(hide_unidentified=False)
if self.sim.held_item
else None
),
vlm_result=vlm_result,
available_commands=self.sim.get_available_commands(),
step_feedback=feedback,
)
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