environment scripting

Existing terrain generation tools prioritize artistic expression and visual realism but lack parametric control, reproducibility, and scriptability—hindering their use in intelligent robotic simulation-driven development, where controllable and explicitly defined terrains are essential. To address this, we propose TerrainGen: a highly modular Python library for procedural terrain generation that integrates rule-based modeling with multi-scale noise synthesis. It enables fine-grained parameterization of physical attributes—including slope, surface roughness, and rock density—and adopts a loosely coupled architecture compatible with Blender for automated rendering and object placement. A declarative configuration interface further simplifies terrain specification. Experimental evaluation demonstrates TerrainGen’s effectiveness in synthetic data generation and perception ground-truth annotation, significantly improving controllability, reproducibility, and deployment efficiency in environment construction. By providing a scalable, programmable infrastructure, TerrainGen advances simulation-based robotics development and machine learning training pipelines.

Existing visualization research predominantly focuses on *how to use* interactive features, neglecting the critical question of *how to construct* them. Method: We propose the first three-layer decoupled interaction authoring task model—intent–technique–component—derived from empirical coding and abstraction of 592 interaction units across 47 real-world applications. Contribution/Results: This model provides descriptive, evaluative, and generative capabilities, enabling the first unified formalization of interaction authoring intent, technical implementation, and component instantiation. It yields a reusable, theory-grounded classification framework that supports critical evaluation of existing visualization tools and informs the design and validation of next-generation low-code interaction authoring systems.

This study addresses the challenge non-programmers face in designing responsive audiovisual interactions for immersive performance. We propose a no-code authoring system built upon a visual logic layer. Methodologically, the system employs a modular architecture that integrates real-time inputs—including pose, spatial position, and speech—and maps them to lighting and sound outputs. To foster deep integration of technology and theatrical practice, we introduce three ensemble collaboration strategies: role rotation, controlled imperfection to stimulate creativity, and technical metaphor as a dramaturgical scaffold. Through six workshops involving eight professional creators, participants developed an improvised, single-audience immersive performance. Results demonstrate that the system significantly lowers the barrier to interactive theatre creation: it enables real-time adjustment during rehearsal and supports improvisational expression—all without coding. The visual logic layer proves both effective and feasible for cross-disciplinary creative practice. (149 words)

Interactive Digital Narrative (IDN) creation faces persistent challenges in harmonizing narrative structure, aesthetic design, and interactive mechanics. Method: This study proposes a collaborative, open-source sandbox framework centered on the “narrative engineer,” introducing a novel “human–environment co-creation” paradigm that dynamically adapts technical capabilities to creators’ goals and expertise. The framework integrates a modular narrative engine, prototype-based content-space modeling, and interactive asset classification and mapping tools to support end-to-end adaptive development—from asset organization to engine customization. Contribution/Results: It significantly enhances creators’ controllability and expressive freedom in designing multi-objective, multi-layered interactive narratives. By providing a systematic, extensible, and reusable architecture, the framework advances IDN authoring tool development and establishes a foundation for scalable, practice-informed narrative engineering.

This study addresses the challenge of effectively evaluating the impact of AI systems in knowledge work, which is hindered by traditional experimental methods that rely on unstructured textual descriptions lacking comparability, reusability, and auditability. To overcome this limitation, the authors propose the SEED framework, which formalizes human–AI collaborative experimental designs as typed participant–process graphs. This approach enables explicit representation of interaction structures, assessment of design novelty, and generation of feasible configurations under specified constraints. Integrating structured encoding, graph-guided generation, and lightweight validation, SEED significantly enhances process clarity, hypothesis specificity, and regulatory compliance in a medical triage task. The results demonstrate its effectiveness as a traceable, comparable, and generative tool for supporting rigorous experimental design in human–AI collaboration.

This work addresses the lack of a general, auditable dynamic control mechanism in existing training systems, which typically rely on framework-specific code. The authors propose the first cross-framework, open-source control plane that exposes training interfaces through a unified protocol, integrating declarative configuration, request validation, and secure control-point scheduling within the Aim workspace to enable metric monitoring, real-time intervention, and operational traceability. The system supports safe human and automated controller interventions during training while fully logging all operational trajectories. Experiments across five NLP and reinforcement learning tasks demonstrate its effectiveness, and the open-source implementation provides a foundation for reproducible human-in-the-loop training.

This work addresses the critical yet often overlooked dependence of agent performance on harness engineering, where control logic is typically entangled within code, hindering transferability, reuse, and systematic study. To overcome this limitation, we propose— for the first time—externalizing the high-level control logic of harnesses into editable, executable natural language specifications, supported by a unified Intelligent Harness Runtime (IHR) architecture. The IHR introduces explicit contracts, persistent artifacts, and lightweight adapters to enable modular harness design and cross-task transfer. We validate our approach on programming and computer-use benchmarks, demonstrating its effectiveness through comprehensive experiments. Ablation studies and successful transfers from code-based to text-based harnesses further illustrate the framework’s flexibility and feasibility.

This work addresses the lack of tools supporting non-technical performers in rapidly prototyping responsive environments during early-stage immersive theater creation. To bridge this gap, the authors propose a rehearsal-oriented, no-code visual system that directly maps sensor inputs—such as gesture, position, and voice—to lighting and sound outputs, enabling creators to configure, test, and iterate interactive spaces in real time during workshops. By abstracting sensing and actuation mechanisms into manipulable “compositional materials,” the system emphasizes visible mappings and low technical barriers, fostering integration between embodied practice and interactive technology. Evaluated across six professional workshops, eight performer-creators successfully employed the system to develop audiovisual scores, trigger-based scenes, responsive architectural prototypes, and multi-room improvisational performances, demonstrating its effectiveness and usability in early creative exploration.