# PointMaze_Medium-v3 goal-directed maze PointMaze_Medium-v3 goal-directed maze. Source-pinned, bounded simulation with explicit goal checks; no physical execution. Record version: `1.0.0` · Hub commit: `ede75c00118e9808e5099da43c26f4619cc3bc37` ## Executable interface ```json { "version": "1", "kind": "task", "abstraction_level": 2, "execution_domain": "simulation", "language": "python", "input_transport": "json_file_or_inline", "output_transport": "json_stdout", "output_selector": "outputs", "conventions": { "linear_unit": "m", "angular_unit": "rad", "time_unit": "s", "coordinate_frame": "right_handed_world_xyz; +z_up", "joint_order": "not_used", "quaternion_order": "not_used" }, "input_schema": { "type": "object", "required": [ "seed" ], "properties": { "seed": { "type": "integer", "minimum": 0, "maximum": 10000 } }, "additionalProperties": false }, "output_schema": { "type": "object", "required": [ "achieved_goal", "desired_goal", "error_m", "steps", "environment_success" ], "properties": { "achieved_goal": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": -20, "maximum": 20 }, "x-unit": "m", "x-frame": "right_handed_world_xyz; +z_up" }, "desired_goal": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": -20, "maximum": 20 }, "x-unit": "m", "x-frame": "right_handed_world_xyz; +z_up" }, "error_m": { "type": "number", "minimum": 0, "maximum": 50, "x-unit": "m" }, "steps": { "type": "integer", "minimum": 1, "maximum": 4000 }, "environment_success": { "type": "boolean" } }, "additionalProperties": false }, "preconditions": [ "Use the exact recorded source, Linux Python 3.10 environment and pinned direct packages.", "No physical robot, network actuation or external robot credentials." ], "postconditions": [ "All reported checks must be true and all output numbers finite.", "Success covers the declared goal and frozen cases, not arbitrary tasks or physical hardware." ], "failure_codes": [ "invalid_input_or_environment", "execution_failed", "task_failed" ], "composition": { "depends_on": [], "automatic_frame_conversion": false, "automatic_hardware_execution": false } } ``` ## Exact environment | Runtime | Robot | Controller | Observation | OS | Python | Evidence | | --- | --- | --- | --- | --- | --- | --- | | gymnasium-robotics 1.4.2 | point-mass | grid_bfs_pd | goal_dict | linux x86_64 | >=3.10,<3.11 | task_complete | Privileged simulator state and declared goals; no perception or real sensor validation. Compatibility status: **hub_verified**. An environment match does not establish task completion. Runtime verification: **task_complete**. ## Source and license - Repository: https://github.com/Farama-Foundation/Gymnasium-Robotics - Source commit: `42eae53b2b27321d29090c219ce2f675c596de77` (no tag) - Original authors: Original gymnasium-robotics contributors (preserve pinned LICENSE) - License: [MIT](https://github.com/Farama-Foundation/Gymnasium-Robotics/blob/42eae53b2b27321d29090c219ce2f675c596de77/LICENSE) - Source archive SHA256: `3c675a90d800b7f1a48710051f106d6c4019f06d859aa928a5cd82e8be141890` - Upstream files: `LICENSE`, `gymnasium_robotics/__init__.py`, `gymnasium_robotics/envs/fetch/fetch_env.py`, `gymnasium_robotics/envs/maze/point_maze.py` - Upstream contribution rules: https://github.com/Farama-Foundation/Gymnasium-Robotics ## Find, inspect, prepare ```sh python /PATH_TO_SKILL/scripts/rsh.py find "task" --runtime gymnasium-robotics --runtime-version 1.4.2 --robot point-mass --json python /PATH_TO_SKILL/scripts/rsh.py inspect gymrobotics-pointmaze-medium-v3 --json python /PATH_TO_SKILL/scripts/rsh.py prepare gymrobotics-pointmaze-medium-v3 --project ./experiment --expected-source-commit 42eae53b2b27321d29090c219ce2f675c596de77 --expected-record-version 1.0.0 --plan --json ``` Remove `--plan` to download the checksum-verified pinned source and create an isolated project environment. Preparation returns the remaining configuration. Dependency installation is an explicit `--install-deps` step; it can execute package build hooks. Robot simulation is a separate command returned by prepare, never run by find/inspect. Direct package pins: `gymnasium==1.2.0`, `gymnasium-robotics==1.4.2`, `mujoco==3.3.7`, `numpy==1.26.4`. Direct runtime versions are pinned and measured. The receipt retains the complete environment freeze; prepare records the installed environment. System drivers are separate host requirements. Hardware: Linux x86_64; CPU simulation; see retained receipt for the exact host environment. Rendering: Headless. EGL libraries are required for MuJoCo imports; PushT uses SDL dummy video. No interactive display.. External API keys: none. ## Invocation contract Entrypoint: `python run.py`. ```json { "argv": [ "{python}", "{recipe_dir}/run.py", "--source-dir", "{source_dir}", "--example" ], "inputs": { "seed": 0 }, "outputs": { "format": "single JSON object", "payload_selector": "outputs", "schema": { "type": "object", "required": [ "achieved_goal", "desired_goal", "error_m", "steps", "environment_success" ], "properties": { "achieved_goal": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": -20, "maximum": 20 }, "x-unit": "m", "x-frame": "right_handed_world_xyz; +z_up" }, "desired_goal": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": -20, "maximum": 20 }, "x-unit": "m", "x-frame": "right_handed_world_xyz; +z_up" }, "error_m": { "type": "number", "minimum": 0, "maximum": 50, "x-unit": "m" }, "steps": { "type": "integer", "minimum": 1, "maximum": 4000 }, "environment_success": { "type": "boolean" } }, "additionalProperties": false } } } ``` Success: All declared goal and finite-state checks pass; exit 0 with task_complete. Native environment success flag after bounded closed-loop control; object/state coordinates are never teleported after reset. Timeout: 180 seconds. Terminate the adapter at the recorded timeout. No physical hardware is opened. Failures: Invalid input or source checksum; Different pinned package version; Unavailable runtime/driver; Goal not reached within bounded steps. ## Actual evidence All 3 frozen cases completed against this exact adapter and source. Reviewer: Hub finite-case Lambda execution harness · 2026-10-20. Commands/checks actually performed: - python tools/verify_runtime_expansion.py --backend gymrobotics --source-cache SOURCE_CACHE --output OUTPUT Submitted evidence and hub verification remain separate. A schema check, PR creation or merge does not establish task success or publication. ## Reusable fixes Change the runtime expansion manifest/template, regenerate, and rerun all frozen cases. Preserve source/model/checkpoint licenses and distinguish simulation from hardware. # PointMaze_Medium-v3 goal-directed maze PointMaze_Medium-v3 goal-directed maze. Source-pinned, bounded simulation with explicit goal checks; no physical execution. Native environment success flag after bounded closed-loop control; object/state coordinates are never teleported after reset. Source and model terms: MIT. The Hub wrapper is MIT. See the record for input JSON, exact environment, frozen execution receipts and preparation requirements.