SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning

• URL: http://arxiv.org/abs/2605.09423v2
• Date: Wed, 13 May 2026 08:34:40 GMT
• Title: SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning
• Abstract summary: SimWorld Studio is an open-source platform built on Unreal Engine 5 for generating evolving embodied learning environments.<n>At its core is SimCoder, a tool/skill-augmented coding agent that writes and executes engine-level code to construct physically grounded 3D worlds.<n> generated worlds are exported as Gym-style environments for embodied agent learning.
• Score: 21.4605333924798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM/VLM-based digital agents have advanced rapidly thanks to scalable sandboxes for coding, web navigation, and computer use, which provide rich interactive training grounds. In contrast, embodied agents still lack abundant, diverse, and automatically generated 3D environments for interactive learning. Existing embodied simulators rely on manually crafted scenes or procedural templates, while recent LLM-based 3D generation systems mainly produce static scenes rather than deployable environments with verifiable tasks and standard learning interfaces. We introduce SimWorld Studio, an open-source platform built on Unreal Engine 5 for generating evolving embodied learning environments. At its core is SimCoder, a tool/skill-augmented coding agent that writes and executes engine-level code to construct physically grounded 3D worlds from language/image instructions. SimCoder self-evolves by using verifier feedback (e.g., compilation errors, physics checks, VLM critiques) to revise environments and autonomously add reusable tools and skills to its library. Generated worlds are exported as Gym-style environments for embodied agent learning. SimWorld Studio further enables co-evolution between environment generation and embodied learning: agent performance feedback guides SimCoder to generate adaptive curricula near the learner's capability frontier, so that environments become increasingly challenging as the embodied agent improves. Three case studies on embodied navigation show that self-evolution improves generation reliability, generated environments substantially improve embodied agent performance that generalizes to unseen benchmarks, and co-evolution yields an 18-point success-rate gain over fixed-environment learning and a 40-point gain over an untrained agent.

Related papers

• SPADE: Self-Play in Adaptive Synthetic Executable Environments [110.80471334001574]
  We introduce SPADE, a self-play RL framework in which a single LLM plays two roles.<n>The Environment Designer writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface.<n>We find several components critical to success: grounding the Environment Designer on documents sampled from a large pretraining corpus, and giving it an accumulated environment memory.
  arXiv  Detail & Related papers  (2026-08-19T17:58:56Z)
• LychSim: A Controllable and Interactive Simulation Framework for Vision Research [13.971578506119647]
  We present LychSim, a highly controllable and interactive simulation framework built upon Unreal Engine 5.<n>LychSim is built around three key designs: (1) a streamlined Python API that abstracts away underlying engine complexities; (2) a procedural data pipeline capable of generating diverse, high-fidelity environments with varying out-of-distribution (OOD) visual challenges, paired with rich 2D and 3D ground truths; and (3) a native integration of the Model Context Protocol (MCP) that transforms the simulator into a dynamic, closed-loop playground for reasoning agentic LLMs.
  arXiv  Detail & Related papers  (2026-05-12T17:40:38Z)
• SAGE: Scalable Agentic 3D Scene Generation for Embodied AI [67.43935343696982]
  Existing scene-generation systems often rely on rule-based or task-specific pipelines, yielding artifacts and physically invalid scenes.<n>We present SAGE, an agentic framework that, given a user-specified embodied task, understands the intent and automatically generates simulation-ready environments at scale.<n>The resulting environments are realistic, diverse, and directly deployable in modern simulators for policy training.
  arXiv  Detail & Related papers  (2026-02-10T18:59:55Z)
• Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning [62.499592503950026]
  Large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments.<n>We propose Agent World Model (AWM), a fully synthetic environment generation pipeline.<n>We scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets.
  arXiv  Detail & Related papers  (2026-02-10T18:55:41Z)
• VirtualEnv: A Platform for Embodied AI Research [26.527818430035534]
  We present VirtualEnv, a next-generation simulation platform built on Unreal Engine 5.<n>It enables fine-grained benchmarking of large language models (LLMs) in embodied and interactive scenarios.<n>We provide a user-friendly API built on top of Unreal Engine, allowing researchers to deploy and control LLM-driven agents.
  arXiv  Detail & Related papers  (2026-01-12T14:04:38Z)
• Procedural Game Level Design with Deep Reinforcement Learning [0.0]
  Procedural content generation (PCG) has become an increasingly popular technique in game development.<n>In this study, a novel method for procedural level design using Deep Reinforcement Learning (DRL) within a Unity-based 3D environment is proposed.
  arXiv  Detail & Related papers  (2025-10-16T20:26:14Z)
• Exploration-Driven Generative Interactive Environments [53.05314852577144]
  We focus on using many virtual environments for inexpensive, automatically collected interaction data.<n>We propose a training framework merely using a random agent in virtual environments.<n>Our agent is fully independent of environment-specific rewards and thus adapts easily to new environments.
  arXiv  Detail & Related papers  (2025-04-03T12:01:41Z)
• Scaling Instructable Agents Across Many Simulated Worlds [70.97268311053328]
  Our goal is to develop an agent that can accomplish anything a human can do in any simulated 3D environment. Our approach focuses on language-driven generality while imposing minimal assumptions. Our agents interact with environments in real-time using a generic, human-like interface.
  arXiv  Detail & Related papers  (2024-03-13T17:50:32Z)
• CoRL: Environment Creation and Management Focused on System Integration [0.0]
  The Core Reinforcement Learning library (CoRL) is a modular, composable, and hyper-configurable environment creation tool. It allows minute control over agent observations, rewards, and done conditions through the use of easy-to-read configuration files, pydantic validators, and a functor design pattern.
  arXiv  Detail & Related papers  (2023-03-03T19:01:53Z)
• Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments [66.83839051693695]
  Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. We propose to leverage recent advances in 3D virtual environments in order to approach the automatic generation of potentially life-long dynamic scenes with photo-realistic appearance. A novel element of this paper is that scenes are described in a parametric way, thus allowing the user to fully control the visual complexity of the input stream the agent perceives.
  arXiv  Detail & Related papers  (2021-09-16T10:37:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.