RoboVerse Architecture Review & Improvement Roadmap#

Document Version: 1.0
Last Updated: January 2026
Status: Active Development

This document provides a comprehensive architecture review of the RoboVerse codebase and outlines a structured improvement roadmap. It is intended for core maintainers and contributors who want to understand the current state of the codebase and contribute to its improvement.

Executive Summary#

Strengths#

RoboVerse is a well-architected multi-simulator robotics framework with several notable strengths:

Aspect	Assessment	Details
Modularity	Excellent	Clear separation between `metasim`, `roboverse_pack`, and `roboverse_learn`
Simulator Abstraction	Good	Unified interface across MuJoCo, IsaacSim, SAPIEN, PyBullet, Genesis
Configuration System	Good	Type-safe `@configclass` decorator with validation
Domain Randomization	Excellent	Comprehensive DR system with hybrid simulation support
Documentation	Good	Extensive tutorials and API documentation

Critical Issues Requiring Attention#

Priority	Issue	Impact	Effort
P0	State cache consistency bug	Data corruption	Low
P0	Test coverage severely lacking	Reliability	High
P1	Configuration system fragmentation	Usability	Medium
P1	Environment creation interface inconsistency	Usability	Medium
P2	Code quality issues	Maintainability	Low

Architecture Overview#

Module Structure#

RoboVerse/
├── metasim/                 # Core simulation framework
│   ├── sim/                 # Simulator handlers (MuJoCo, Isaac, SAPIEN, etc.)
│   ├── scenario/            # Scene configuration (robots, objects, cameras)
│   ├── task/                # Task environment abstraction
│   ├── randomization/       # Domain randomization system
│   ├── queries/             # Extended query system (contacts, sensors)
│   └── utils/               # Utilities (configclass, math, state conversion)
│
├── roboverse_pack/          # Assets and task definitions
│   ├── robots/              # 50+ robot configurations
│   ├── tasks/               # 200+ task environments
│   ├── scenes/              # Scene configurations
│   └── queries/             # Custom query implementations
│
├── roboverse_learn/         # Learning algorithms
│   ├── il/                  # Imitation learning (ACT, Diffusion Policy, etc.)
│   ├── rl/                  # Reinforcement learning (PPO, TD3, SAC)
│   └── vla/                 # Vision-Language-Action models
│
└── generation/              # Asset generation and conversion tools

Core Abstractions#

┌─────────────────────────────────────────────────────────────────┐
│                        BaseSimHandler                            │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │ MujocoHandler│  │IsaacHandler │  │SAPIENHandler│  ...        │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                        BaseTaskEnv                               │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │PickPlaceTask│  │LocomotionTask│  │ManipulationTask│  ...     │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                        ScenarioCfg                               │
│  robots[] + objects[] + cameras[] + lights[] + sim_params       │
└─────────────────────────────────────────────────────────────────┘

Identified Issues#

Issue 1: State Cache Consistency Bug (P0 - Critical)#

Location: metasim/sim/base.py:118-129

Problem: The state cache is modified in-place when switching between tensor and dict modes, causing data corruption.

# Current problematic implementation
def get_states(self, env_ids=None, mode="tensor"):
    if self._state_cache_expire:
        self._states = self._get_states(env_ids=env_ids)
        self._state_cache_expire = False
    # BUG: This overwrites the cache!
    if isinstance(self._states, TensorState) and mode == "dict":
        self._states = state_tensor_to_nested(self, self._states)
    elif isinstance(self._states, list) and mode == "tensor":
        self._states = list_state_to_tensor(self, self._states)
    return self._states

Reproduction:

handler = create_handler(...)
states_t1 = handler.get_states(mode="tensor")  # TensorState
states_d = handler.get_states(mode="dict")     # Converts cache to dict
states_t2 = handler.get_states(mode="tensor")  # Returns dict, not tensor!

Impact: Silent data corruption in training pipelines that use both state formats.

Solution: See TODO-001.

Issue 2: Incomplete Abstract Method Declarations (P1)#

Location: metasim/sim/base.py:86-91

Problem: _set_dof_targets() should be marked as @abstractmethod but is commented out.

# @abstractmethod  # <-- This is commented out!
def _set_dof_targets(self, actions: list[Action]) -> None:
    raise NotImplementedError

Impact:

Static analysis tools cannot detect missing implementations
IDE cannot provide proper warnings
New simulator implementations may forget to implement this method

Issue 3: Configuration System Fragmentation (P1)#

Problem: Three different configuration systems are used across modules:

Module	System	Tools
`metasim`	`@configclass`	Python dataclass + custom wrapper
`roboverse_learn/il`	Hydra	YAML + OmegaConf
`roboverse_learn/rl`	Mixed	YAML + tyro + argparse

Impact:

Steep learning curve for contributors
Configuration cannot be easily shared between modules
Inconsistent user experience

Issue 4: Test Coverage Severely Lacking (P0)#

Current Coverage Estimate:

Module	Test Files	Estimated Coverage
`metasim`	29	~60-70%
`roboverse_pack`	0	0%
`roboverse_learn`	1	<5%
`generation`	1	~20%
Overall	31	~15-20%

Critical Gaps:

50+ robot configurations have no validation tests
200+ task environments have no tests
All learning algorithms (ACT, Diffusion Policy, PPO, TD3) have no tests
No code coverage reporting in CI

Issue 5: Environment Creation Interface Inconsistency (P1)#

Problem: Two different ways to create environments:

# Method 1: Gymnasium style (used in clean_rl, vla)
from gymnasium import make_vec
env = make_vec("RoboVerse/task", robots=[...], simulator=sim)

# Method 2: Direct task class (used in fast_td3, rsl_rl, il)
from metasim.task.registry import get_task_class
env = get_task_class(task)(scenario)

Impact: Confusion for new users; inconsistent integration patterns.

Issue 6: Code Quality Issues (P2)#

Issue	Location	Count
Commented-out code	Multiple files	~15 instances
Magic numbers	`sim/isaacsim/`, `sim/mujoco/`	~20 instances
Missing type annotations	Various	~100+ functions
Inconsistent error handling	All simulator handlers	Varies

Issue 7: Parallel Simulation Error Handling (P1)#

Location: metasim/sim/parallel.py:127-132

Problem: _check_error() is only called at specific times, potentially missing subprocess errors.

def _check_error(self):
    # Only checks for errors in error queue
    # May miss errors if not called at right time

Impact: Silent failures in parallel training runs.

Improvement Roadmap#

Phase 1: Critical Fixes (Weeks 1-2)#

┌────────────────────────────────────────────────────────┐
│ PHASE 1: Critical Fixes                                │
│                                                        │
│ □ TODO-001: Fix state cache consistency                │
│ □ TODO-002: Add @abstractmethod decorators             │
│ □ TODO-003: Add robot configuration validation tests   │
│ □ TODO-004: Integrate pytest-cov in CI                 │
└────────────────────────────────────────────────────────┘

Phase 2: Test Coverage (Weeks 3-6)#

┌────────────────────────────────────────────────────────┐
│ PHASE 2: Test Coverage Improvement                     │
│                                                        │
│ □ TODO-005: Add task environment integration tests     │
│ □ TODO-006: Add learning algorithm unit tests          │
│ □ TODO-007: Add state conversion tests                 │
│ □ TODO-008: Add domain randomization tests             │
└────────────────────────────────────────────────────────┘

Phase 3: Interface Unification (Weeks 7-10)#

┌────────────────────────────────────────────────────────┐
│ PHASE 3: Interface Unification                         │
│                                                        │
│ □ TODO-009: Create unified environment factory         │
│ □ TODO-010: Standardize configuration loading          │
│ □ TODO-011: Unify error handling across simulators     │
│ □ TODO-012: Add deprecation warnings for old APIs      │
└────────────────────────────────────────────────────────┘

Phase 4: Code Quality (Weeks 11-14)#

┌────────────────────────────────────────────────────────┐
│ PHASE 4: Code Quality                                  │
│                                                        │
│ □ TODO-013: Remove commented-out code                  │
│ □ TODO-014: Extract magic numbers to constants         │
│ □ TODO-015: Add comprehensive type annotations         │
│ □ TODO-016: Add performance benchmarks                 │
└────────────────────────────────────────────────────────┘

Phase 5: Architecture Evolution (Long-term)#

┌────────────────────────────────────────────────────────┐
│ PHASE 5: Architecture Evolution                        │
│                                                        │
│ □ TODO-017: Implement plugin architecture for sims     │
│ □ TODO-018: Create unified configuration system        │
│ □ TODO-019: Add async simulation support               │
│ □ TODO-020: Performance profiling integration          │
└────────────────────────────────────────────────────────┘

Detailed TODO List#

TODO-001: Fix State Cache Consistency#

Priority: P0 (Critical)
Effort: Low (1-2 days)
Risk: Low

Description: Fix the state cache to maintain separate caches for tensor and dict formats.

Implementation:

# metasim/sim/base.py

class BaseSimHandler(ABC):
    def __init__(self, scenario, optional_queries=None):
        # ... existing code ...
        self._state_cache_expire = True
        self._tensor_state_cache: TensorState | None = None
        self._dict_state_cache: list[DictEnvState] | None = None

    def _invalidate_cache(self) -> None:
        """Invalidate all state caches."""
        self._state_cache_expire = True
        self._tensor_state_cache = None
        self._dict_state_cache = None

    def set_states(self, states, env_ids=None) -> None:
        """Set states and invalidate cache."""
        self._invalidate_cache()
        self._set_states(states, env_ids)

    def get_states(
        self, 
        env_ids: list[int] | None = None, 
        mode: Literal["tensor", "dict"] = "tensor"
    ) -> TensorState | list[DictEnvState]:
        """Get states with independent caching for each mode."""
        if self._state_cache_expire:
            self._tensor_state_cache = self._get_states(env_ids=env_ids)
            self._dict_state_cache = None  # Lazy conversion
            self._state_cache_expire = False

        if mode == "tensor":
            return self._tensor_state_cache
        else:
            if self._dict_state_cache is None:
                self._dict_state_cache = state_tensor_to_nested(
                    self, self._tensor_state_cache
                )
            return self._dict_state_cache

Test Case:

# metasim/test/sim/test_state_cache.py

@pytest.mark.general
def test_state_cache_mode_independence():
    """Verify that switching modes doesn't corrupt cache."""
    handler = create_test_handler()
    handler.launch()
    
    # Get tensor state
    states_t1 = handler.get_states(mode="tensor")
    assert isinstance(states_t1, TensorState)
    
    # Get dict state (should not affect tensor cache)
    states_d = handler.get_states(mode="dict")
    assert isinstance(states_d, list)
    
    # Get tensor state again (should return same type)
    states_t2 = handler.get_states(mode="tensor")
    assert isinstance(states_t2, TensorState)
    
    # Values should match
    assert torch.allclose(states_t1.pos, states_t2.pos)

Acceptance Criteria:

Test case passes
Existing tests still pass
No breaking changes to public API

TODO-002: Add @abstractmethod Decorators#

Priority: P1
Effort: Very Low (1 hour)
Risk: Very Low

Files to modify:

metasim/sim/base.py

Changes:

# Before
# @abstractmethod
def _set_dof_targets(self, actions: list[Action]) -> None:
    raise NotImplementedError

# After
@abstractmethod
def _set_dof_targets(self, actions: list[Action]) -> None:
    """Set DOF targets. Subclasses must implement this method."""
    raise NotImplementedError

Verification:

# Run mypy to verify abstract method detection
mypy metasim/sim/base.py

TODO-003: Add Robot Configuration Validation Tests#

Priority: P0
Effort: Medium (2-3 days)
Risk: Low

Implementation:

# metasim/test/test_robot_configs.py

import pytest
from pathlib import Path
import importlib
import pkgutil

def get_all_robot_configs():
    """Dynamically discover all robot configuration classes."""
    import roboverse_pack.robots as robots_module
    
    configs = []
    for importer, modname, ispkg in pkgutil.iter_modules(robots_module.__path__):
        if modname.endswith('_cfg'):
            module = importlib.import_module(f'roboverse_pack.robots.{modname}')
            for name in dir(module):
                obj = getattr(module, name)
                if (isinstance(obj, type) and 
                    hasattr(obj, 'name') and 
                    name.endswith('Cfg')):
                    configs.append(obj)
    return configs

ALL_ROBOT_CONFIGS = get_all_robot_configs()

@pytest.mark.general
@pytest.mark.parametrize("robot_cfg_cls", ALL_ROBOT_CONFIGS, 
                         ids=lambda x: x.__name__)
def test_robot_config_instantiation(robot_cfg_cls):
    """Verify robot config can be instantiated."""
    cfg = robot_cfg_cls()
    assert cfg.name is not None
    assert isinstance(cfg.name, str)
    assert len(cfg.name) > 0

@pytest.mark.general
@pytest.mark.parametrize("robot_cfg_cls", ALL_ROBOT_CONFIGS,
                         ids=lambda x: x.__name__)
def test_robot_config_has_asset_path(robot_cfg_cls):
    """Verify robot config has at least one asset path."""
    cfg = robot_cfg_cls()
    
    asset_paths = [
        getattr(cfg, 'usd_path', None),
        getattr(cfg, 'urdf_path', None),
        getattr(cfg, 'mjcf_path', None),
    ]
    
    valid_paths = [p for p in asset_paths if p is not None and len(p) > 0]
    assert len(valid_paths) > 0, f"{cfg.name} has no valid asset path"

@pytest.mark.general
@pytest.mark.parametrize("robot_cfg_cls", ALL_ROBOT_CONFIGS,
                         ids=lambda x: x.__name__)
def test_robot_config_has_actuators(robot_cfg_cls):
    """Verify robot config has actuator definitions."""
    cfg = robot_cfg_cls()
    
    if hasattr(cfg, 'actuators'):
        assert len(cfg.actuators) > 0, f"{cfg.name} has no actuators defined"

@pytest.mark.general
@pytest.mark.parametrize("robot_cfg_cls", ALL_ROBOT_CONFIGS,
                         ids=lambda x: x.__name__)
def test_robot_config_joint_limits_valid(robot_cfg_cls):
    """Verify joint limits are valid (lower < upper)."""
    cfg = robot_cfg_cls()
    
    if hasattr(cfg, 'joint_limits'):
        for joint_name, (lower, upper) in cfg.joint_limits.items():
            assert lower < upper, \
                f"{cfg.name}.{joint_name}: lower ({lower}) >= upper ({upper})"

TODO-004: Integrate pytest-cov in CI#

Priority: P0
Effort: Low (1 day)
Risk: Very Low

Changes to .github/workflows/premerge-ci.yml:

# Add coverage reporting step
- name: Run tests with coverage
  run: |
    pytest metasim/test \
      --cov=metasim \
      --cov-report=xml \
      --cov-report=html \
      --cov-fail-under=30 \
      -k ${{ matrix.test_type }}

- name: Upload coverage to Codecov
  uses: codecov/codecov-action@v4
  with:
    file: ./coverage.xml
    flags: ${{ matrix.test_type }}
    fail_ci_if_error: false

Add pyproject.toml configuration:

[tool.coverage.run]
source = ["metasim"]
omit = ["metasim/test/*", "*/__pycache__/*"]

[tool.coverage.report]
exclude_lines = [
    "pragma: no cover",
    "raise NotImplementedError",
    "if TYPE_CHECKING:",
]

TODO-005: Add Task Environment Integration Tests#

Priority: P1
Effort: High (1 week)
Risk: Medium

Implementation:

# metasim/test/test_task_environments.py

import pytest
from metasim.task.registry import get_task_class, TASK_REGISTRY

# Select representative tasks for testing
CORE_TASKS = [
    "pick_cube",
    "place_cube", 
    "open_drawer",
    "close_drawer",
    "push_button",
]

@pytest.mark.mujoco
@pytest.mark.parametrize("task_name", CORE_TASKS)
def test_task_reset_step_mujoco(task_name):
    """Test basic reset/step cycle for core tasks on MuJoCo."""
    task_cls = get_task_class(task_name)
    scenario = task_cls.scenario.copy()
    scenario.update(
        simulator="mujoco",
        num_envs=1,
        headless=True,
    )
    
    env = task_cls(scenario, device="cpu")
    env.launch()
    
    try:
        # Test reset
        obs, info = env.reset()
        assert obs is not None
        
        # Test step
        action = env.action_space.sample()
        obs, reward, terminated, truncated, info = env.step(action)
        
        assert obs is not None
        assert isinstance(reward, (int, float, torch.Tensor))
        assert isinstance(terminated, (bool, torch.Tensor))
        assert isinstance(truncated, (bool, torch.Tensor))
    finally:
        env.close()

TODO-006: Add Learning Algorithm Unit Tests#

Priority: P1
Effort: High (1-2 weeks)
Risk: Medium

Example for Diffusion Policy:

# roboverse_learn/il/tests/test_diffusion_policy.py

import pytest
import torch
from roboverse_learn.il.policies.dp.ddpm_dit_image_policy import DDPMDiTImagePolicy

@pytest.fixture
def sample_obs():
    """Create sample observation for testing."""
    return {
        "image": torch.randn(1, 3, 224, 224),
        "agent_pos": torch.randn(1, 7),
    }

def test_diffusion_policy_forward():
    """Test forward pass of diffusion policy."""
    policy = DDPMDiTImagePolicy(
        obs_dim=7,
        action_dim=7,
        horizon=16,
        # ... minimal config
    )
    
    obs = sample_obs()
    action = policy.predict_action(obs)
    
    assert action.shape == (1, 16, 7)  # (batch, horizon, action_dim)

def test_diffusion_policy_training_step():
    """Test single training step."""
    policy = DDPMDiTImagePolicy(...)
    optimizer = torch.optim.Adam(policy.parameters())
    
    batch = create_training_batch()
    loss = policy.compute_loss(batch)
    
    assert loss.requires_grad
    loss.backward()
    optimizer.step()

TODO-009: Create Unified Environment Factory#

Priority: P1
Effort: Medium (3-5 days)
Risk: Medium

Implementation:

# metasim/env_factory.py

from typing import Union, List, Optional
from metasim.scenario.robot import RobotCfg

def make_env(
    task: str,
    robots: Optional[List[Union[str, RobotCfg]]] = None,
    simulator: str = "mujoco",
    num_envs: int = 1,
    headless: bool = True,
    device: str = "cuda",
    cameras: Optional[List] = None,
    **kwargs
):
    """
    Unified environment factory for RoboVerse.
    
    This is the recommended way to create environments.
    
    Args:
        task: Task name (e.g., "pick_cube", "locomotion_walk")
        robots: List of robot names or RobotCfg instances
        simulator: Simulator backend ("mujoco", "isaacsim", "sapien3", etc.)
        num_envs: Number of parallel environments
        headless: Whether to run in headless mode
        device: Device for tensor computations
        cameras: Camera configurations for observations
        **kwargs: Additional task-specific arguments
    
    Returns:
        BaseTaskEnv: Configured environment instance
    
    Example:
        >>> env = make_env(
        ...     task="pick_cube",
        ...     robots=["franka"],
        ...     simulator="mujoco",
        ...     num_envs=16,
        ... )
        >>> obs, info = env.reset()
        >>> obs, reward, term, trunc, info = env.step(action)
    """
    from metasim.task.registry import get_task_class
    from metasim.utils.setup_util import get_robot
    
    # Resolve task class
    task_cls = get_task_class(task)
    
    # Build scenario from task default
    scenario = task_cls.scenario.copy()
    
    # Resolve robots
    if robots is not None:
        resolved_robots = []
        for robot in robots:
            if isinstance(robot, str):
                resolved_robots.append(get_robot(robot))
            else:
                resolved_robots.append(robot)
        scenario.robots = resolved_robots
    
    # Apply overrides
    scenario.update(
        simulator=simulator,
        num_envs=num_envs,
        headless=headless,
        cameras=cameras or [],
    )
    
    # Create and return environment
    env = task_cls(scenario, device=device, **kwargs)
    env.launch()
    
    return env

# Also register with gymnasium for compatibility
def register_gymnasium_envs():
    """Register all RoboVerse tasks with Gymnasium."""
    import gymnasium
    from metasim.task.registry import TASK_REGISTRY
    
    for task_name in TASK_REGISTRY:
        gymnasium.register(
            id=f"RoboVerse/{task_name}",
            entry_point="metasim.env_factory:make_env",
            kwargs={"task": task_name},
        )

TODO-013: Remove Commented-Out Code#

Priority: P2
Effort: Very Low (2 hours)
Risk: Very Low

Files to clean:

File	Line	Content
`metasim/sim/base.py`	18	`# from metasim.utils.hf_util import FileDownloader`
`metasim/sim/base.py`	36	`# FileDownloader(scenario).do_it()`
`metasim/sim/base.py`	86	`# @abstractmethod`
`metasim/scenario/scenario.py`	72	Various commented code

Approach:

Search for # patterns followed by code
Review each instance
Either remove or convert to proper TODO comment

TODO-014: Extract Magic Numbers to Constants#

Priority: P2
Effort: Low (1-2 days)
Risk: Low

Create constants file:

# metasim/constants.py

"""Framework-wide constants."""

# Simulation defaults
DEFAULT_DT = 0.015  # 15ms physics timestep
DEFAULT_DECIMATION = 2
DEFAULT_GRAVITY = (0.0, 0.0, -9.81)

# Cache settings
STATE_CACHE_SIZE = 1000
MAX_PARALLEL_ENVS = 4096

# Timeouts
DEFAULT_LAUNCH_TIMEOUT = 30.0
DEFAULT_STEP_TIMEOUT = 5.0

# Numerical tolerances
POSITION_TOLERANCE = 1e-5
ROTATION_TOLERANCE = 1e-4

Implementation Guidelines#

Safe Modification Protocol#

Before making any changes, follow this checklist:

□ Pre-modification
  ├── □ Read existing tests for the module
  ├── □ Run existing tests (ensure they pass)
  ├── □ Understand the change's impact scope
  └── □ Check for downstream dependencies

□ Implementation
  ├── □ Write tests first (TDD preferred)
  ├── □ Make small, focused commits
  ├── □ Maintain backward compatibility
  └── □ Add deprecation warnings if needed

□ Post-modification
  ├── □ Run full test suite locally
  ├── □ Check for linter errors
  ├── □ Update documentation if needed
  └── □ Create detailed PR description

Feature Flag Pattern#

For high-risk changes, use feature flags:

# metasim/config.py

class FeatureFlags:
    """Feature flags for gradual rollout of changes."""
    
    # State cache v2 with independent tensor/dict caches
    USE_INDEPENDENT_STATE_CACHE = False
    
    # New unified environment factory
    USE_UNIFIED_ENV_FACTORY = False
    
    # Strict type checking in configs
    STRICT_CONFIG_VALIDATION = False

Deprecation Pattern#

import warnings
from functools import wraps

def deprecated(message: str, removal_version: str = "0.3.0"):
    """Decorator to mark functions as deprecated."""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            warnings.warn(
                f"{func.__name__} is deprecated: {message}. "
                f"Will be removed in version {removal_version}.",
                DeprecationWarning,
                stacklevel=2
            )
            return func(*args, **kwargs)
        return wrapper
    return decorator

# Usage
@deprecated("Use make_env() instead", removal_version="0.4.0")
def create_environment_legacy(...):
    ...

Testing Strategy#

Test Hierarchy#

┌─────────────────────────────────────────────────────────────┐
│                    End-to-End Tests                         │
│              (Full training pipeline tests)                 │
│                      ~5% of tests                           │
├─────────────────────────────────────────────────────────────┤
│                   Integration Tests                         │
│         (Task reset/step, multi-env, rendering)             │
│                     ~25% of tests                           │
├─────────────────────────────────────────────────────────────┤
│                      Unit Tests                             │
│    (Individual functions, state conversion, configs)        │
│                     ~70% of tests                           │
└─────────────────────────────────────────────────────────────┘

Running Tests Locally#

# Run all general tests (no simulator required)
pytest metasim/test -k general -v

# Run MuJoCo tests
pytest metasim/test -k mujoco -v

# Run with coverage
pytest metasim/test --cov=metasim --cov-report=html

# Run specific test file
pytest metasim/test/sim/test_state_cache.py -v

Coverage Targets#

Phase	Target Coverage	Timeline
Current	~15-20%	-
Phase 1	30%	Week 2
Phase 2	50%	Week 6
Phase 3	60%	Week 10
Long-term	70%+	Week 14+

Appendix: Quick Reference#

Priority Definitions#

Priority	Definition	Response Time
P0	Critical - blocks users or causes data corruption	Immediate
P1	High - significant usability or reliability issue	1-2 weeks
P2	Medium - code quality or maintainability	2-4 weeks
P3	Low - nice to have improvements	Backlog

Effort Definitions#

Effort	Time Estimate	Description
Very Low	< 4 hours	Single file, obvious change
Low	1-2 days	Few files, clear scope
Medium	3-5 days	Multiple files, some complexity
High	1-2 weeks	Significant refactoring
Very High	2+ weeks	Architecture-level changes

Contributing#

If you want to contribute to any of these improvements:

Comment on the relevant GitHub issue (or create one)
Follow the Safe Modification Protocol
Start with lower-risk items to familiarize yourself with the codebase
Ask questions in discussions if anything is unclear

Recommended starting points for new contributors:

TODO-002: Add @abstractmethod decorators (Very Low effort)
TODO-013: Remove commented-out code (Very Low effort)
TODO-003: Add robot configuration validation tests (Medium effort, high impact)

RoboVerse Architecture Review & Improvement Roadmap#

Table of Contents#

Executive Summary#

Strengths#

Critical Issues Requiring Attention#

Architecture Overview#

Module Structure#

Core Abstractions#

Identified Issues#

Issue 1: State Cache Consistency Bug (P0 - Critical)#

Issue 2: Incomplete Abstract Method Declarations (P1)#

Issue 3: Configuration System Fragmentation (P1)#

Issue 4: Test Coverage Severely Lacking (P0)#

Issue 5: Environment Creation Interface Inconsistency (P1)#

Issue 6: Code Quality Issues (P2)#

Issue 7: Parallel Simulation Error Handling (P1)#

Improvement Roadmap#

Phase 1: Critical Fixes (Weeks 1-2)#

Phase 2: Test Coverage (Weeks 3-6)#

Phase 3: Interface Unification (Weeks 7-10)#

Phase 4: Code Quality (Weeks 11-14)#

Phase 5: Architecture Evolution (Long-term)#

Detailed TODO List#

TODO-001: Fix State Cache Consistency#

TODO-002: Add @abstractmethod Decorators#

TODO-003: Add Robot Configuration Validation Tests#

TODO-004: Integrate pytest-cov in CI#

TODO-005: Add Task Environment Integration Tests#

TODO-006: Add Learning Algorithm Unit Tests#

TODO-009: Create Unified Environment Factory#

TODO-013: Remove Commented-Out Code#

TODO-014: Extract Magic Numbers to Constants#

Implementation Guidelines#

Safe Modification Protocol#

Feature Flag Pattern#

Deprecation Pattern#

Testing Strategy#

Test Hierarchy#

Running Tests Locally#

Coverage Targets#

Appendix: Quick Reference#

Priority Definitions#

Effort Definitions#

Contributing#

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