FetchPush-v4 goal-directed push

FetchPush-v4 goal-directed push. Source-pinned, bounded simulation with explicit goal checks; no physical execution.

gymrobotics-fetchpush-v4 | Record version: 1.0.0

Record JSON | Markdown recipe | Complete search index

Fetch and prepare

curl -fsSL https://hub.superfastrobotics.com/skills/gymrobotics-fetchpush-v4/skill.json
curl -fsSL https://hub.superfastrobotics.com/skills/gymrobotics-fetchpush-v4/README.md
python /PATH_TO_SKILL/scripts/rsh.py inspect gymrobotics-fetchpush-v4 --json
python /PATH_TO_SKILL/scripts/rsh.py prepare gymrobotics-fetchpush-v4 --project ./experiment --expected-source-commit 42eae53b2b27321d29090c219ce2f675c596de77 --expected-record-version 1.0.0 --plan --json

Inspect the requirements, then remove --plan to prepare a project. Preparation verifies the archive SHA256 and preserves exact source pins. --install-deps explicitly installs dependencies. Run the returned simulation command separately.

Environment

Runtime: gymnasium-robotics 1.4.2
Robot: fetch
Controller: cartesian_delta_feedback
Observation: goal_dict
OS / architecture: linux / x86_64
Python: >=3.10,<3.11

Compatibility status: hub_verified

Privileged simulator state and declared goals; no perception or real sensor validation.

Requirements

Linux x86_64; CPU simulation, tested on a Lambda A100 host

Headless. EGL libraries are required for MuJoCo imports; PushT uses SDL dummy video. No interactive display.

Packages: 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.

External API keys: none.

Invocation

Executable interface

{
  "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": 3,
        "maxItems": 3,
        "items": {
          "type": "number",
          "minimum": -20,
          "maximum": 20
        },
        "x-unit": "m",
        "x-frame": "right_handed_world_xyz; +z_up"
      },
      "desired_goal": {
        "type": "array",
        "minItems": 3,
        "maxItems": 3,
        "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": 1000
      },
      "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
  }
}

Entrypoint: python run.py

{
  "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": 3,
          "maxItems": 3,
          "items": {
            "type": "number",
            "minimum": -20,
            "maximum": 20
          },
          "x-unit": "m",
          "x-frame": "right_handed_world_xyz; +z_up"
        },
        "desired_goal": {
          "type": "array",
          "minItems": 3,
          "maxItems": 3,
          "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": 1000
        },
        "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.

Failure modes

Invalid input or source checksum
Different pinned package version
Unavailable runtime/driver
Goal not reached within bounded steps

Source and license

Repository: https://github.com/Farama-Foundation/Gymnasium-Robotics
Authors: Original gymnasium-robotics contributors (preserve pinned LICENSE)
Commit: 42eae53b2b27321d29090c219ce2f675c596de77
Archive SHA256: 3c675a90d800b7f1a48710051f106d6c4019f06d859aa928a5cd82e8be141890
License: MIT
Hub commit: b0414668be840daa6a6210723c298b31d6dd6d8c

Evidence

Runtime: task_complete / task_complete

All 3 frozen cases completed against this exact adapter and source.

Reviewer: Hub finite-case Lambda execution harness | 2026-10-20

python tools/verify_runtime_expansion.py --backend gymrobotics --source-cache SOURCE_CACHE --output OUTPUT

Submitter reports and hub verification remain separate. Source review does not establish task success.