Author name cluster

Zhen Luo

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5 papers

2 author rows

IROS Conference 2025 Conference Paper

LLplace: Embodied 3D Indoor Layout Synthesis Framework with Large Language Model

Yixuan Yang
Junru Lu
Zixiang Zhao
Zhen Luo
Wanxi Dong
Victor Sanchez
Feng Zheng 0001

Designing 3D indoor layouts is a crucial task with significant applications in embodied robot intelligence, virtual reality, and interior design. Existing methods for 3D layout design either rely on diffusion models, which utilize spatial relationship priors, or heavily leverage the inferential capabilities of proprietary Large Language Models (LLMs), which require extensive prompt engineering and in-context exemplars via black-box trials. These methods often face limitations in generalization and dynamic scene editing. In this paper, we introduce LLplace, a novel 3D indoor scene layout designer based on lightweight, fine-tuned, open-source LLM Llama3. LLplace circumvents the need for spatial relationship priors and in-context exemplars, enabling efficient and credible room layout generation based solely on user inputs specifying the room type and desired objects. We curated a new dialogue dataset based on the 3D-Front dataset, expanding the original data volume and incorporating dialogue data for adding and removing objects. This dataset can enhance the LLM’s spatial understanding. Furthermore, through dialogue, LLplace activates the LLM’s capability to understand 3D layouts and perform dynamic scene editing, enabling the addition and removal of objects. Our approach demonstrates that LLplace can effectively generate and edit 3D indoor layouts interactively and outperform existing methods in delivering high-quality 3D design solutions.

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NeurIPS Conference 2025 Conference Paper

MesaTask: Towards Task-Driven Tabletop Scene Generation via 3D Spatial Reasoning

Jinkun Hao
Naifu Liang
Zhen Luo
Xudong XU
Weipeng Zhong
Ran Yi
Yichen Jin
Zhaoyang Lyu

The ability of robots to interpret human instructions and execute manipulation tasks necessitates the availability of task-relevant tabletop scenes for training. However, traditional methods for creating these scenes rely on time-consuming manual layout design or purely randomized layouts, which are limited in terms of plausibility or alignment with the tasks. In this paper, we formulate a novel task, namely task-oriented tabletop scene generation, which poses significant challenges due to the substantial gap between high-level task instructions and the tabletop scenes. To support research on such a challenging task, we introduce \textbf{MesaTask-10K}, a large-scale dataset comprising approximately 10, 700 synthetic tabletop scenes with \emph{manually crafted layouts} that ensure realistic layouts and intricate inter-object relations. To bridge the gap between tasks and scenes, we propose a \textbf{Spatial Reasoning Chain} that decomposes the generation process into object inference, spatial interrelation reasoning, and scene graph construction for the final 3D layout. We present \textbf{MesaTask}, an LLM-based framework that utilizes this reasoning chain and is further enhanced with DPO algorithms to generate physically plausible tabletop scenes that align well with given task descriptions. Exhaustive experiments demonstrate the superior performance of MesaTask compared to baselines in generating task-conforming tabletop scenes with realistic layouts.

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NeurIPS Conference 2025 Conference Paper

OptiScene: LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization

Yixuan Yang
Zhen Luo
Tongsheng Ding
Junru Lu
Mingqi Gao
Jinyu Yang
Victor Sanchez
Feng Zheng

Automatic indoor layout generation has attracted increasing attention due to its potential in interior design, virtual environment construction, and embodied AI. Existing methods fall into two categories: prompt-driven approaches that leverage proprietary LLM services (e. g. , GPT APIs), and learning-based methods trained on layout data upon diffusion-based models. Prompt-driven methods often suffer from spatial inconsistency and high computational costs, while learning-based methods are typically constrained by coarse relational graphs and limited datasets, restricting their generalization to diverse room categories. In this paper, we revisit LLM-based indoor layout generation and present 3D-SynthPlace, a large-scale dataset that combines synthetic layouts generated via a `GPT synthesize, Human inspect' pipeline, upgraded from the 3D-Front dataset. 3D-SynthPlace contains nearly 17, 000 scenes, covering four common room types—bedroom, living room, kitchen, and bathroom—enriched with diverse objects and high-level spatial annotations. We further introduce OptiScene, a strong open-source LLM optimized for indoor layout generation, fine-tuned based on our 3D-SynthPlace dataset through our two-stage training. For the warum-up stage I, we adopt supervised fine-tuning (SFT), which is taught to first generate high-level spatial descriptions then conditionally predict concrete object placements. For the reinforcing stage II, to better align the generated layouts with human design preferences, we apply multi-turn direct preference optimization (DPO), which significantly improving layout quality and generation success rates. Extensive experiments demonstrate that OptiScene outperforms traditional prompt-driven and learning-based baselines. Moreover, OptiScene shows promising potential in interactive tasks such as scene editing and robot navigation, highlighting its applicability beyond static layout generation.

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IROS Conference 2022 Conference Paper

Bioinspired Antagonist-agonist Artificial Muscles for Humanoid Eyeball Motions

Zhen Luo
Zhipeng Xu
Jisen Li
Jian Zhu 0005

Natural eyeball motions in humanoid robots can contribute to friendly communication, thus improving the human-robot interaction. In this paper, we develop antagonist-agonist artificial muscles for humanoid eyeball motions, by using dielectric elastomer actuators (DEAs). Inspired by human eyeballs, the artificial muscles consist of two pairs of DEA: one pair for the horizontal motion, and the other for the vertical motion. The fabrication time of actuators can be significantly decreased due to their simple structure. The antagonist-agonist actuator outperforms the dielectric elastomer minimum energy structure in terms of actuation displacement and response time. We conduct experiments in a lifesize human face model. The experiments demonstrate the capability of antagonist-agonist artificial muscles to mimic eyeball motions in the horizontal, vertical, and diagonal directions. Future work includes modeling and control of artificial muscles for optimal performance of various humanoid eyeball motions.

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IROS Conference 2006 Conference Paper

Topology Optimization of Compliant Mechanisms Using Sequential Convex Programming

Hua Ying
Hongmei Xu
Zhen Luo

This paper has presented a novel multi-criteria formulation for the topology optimization design of compliant mechanisms by using sequential convex programming approach. Both structural strain energy and mechanical efficiency are shaped together through compromise programming method to develop a new combined formulation to meet the optimal design according to the demands of minimizing structural compliance and maximizing mechanical function specification. The artificial spring model is resorted to reveal the relationship between the work-piece and the compliant mechanism, and the dummy load method is used to further engrave the formulation pattern by superposition of two load cases. Displacement limitation and material usage are imposed as external constraints to narrow the design domain. A sequential convex programming using the approximations of the method of moving asymptotes (MMA) is applied to solve the optimization problem. SIMP interpolation scheme is used to indicate the dependence between the elastic modulus upon regularized element densities. Numerical examples have been applied to demonstrate the validation of the proposed methods

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