3D indoor scene generation aims to generate scenes that are physically plausible, consistent with common sense, and well-aligned with user intent. However, existing methods struggle to effectively capture user intent, as coarse-grained instruction methods yield plausible but intent-missing layouts, while fine-grained instruction methods reflect user intent but rely on manually defined relationships that burden users and compromise physical plausibility in complex scenes. To address this challenge, we propose SDGScenes, a novel indoor scene generation framework that automatically infers and synthesizes complete scenes from user intent and commonsense knowledge. Our approach firstly encodes scene requirements using Semantic Dependency Graph (SDG), a representation that captures both user intent module and commonsense module relationships. Sequentially, guided by the SDG, a Vision-Language Model (VLM) infers spatial constraints through commonsense reasoning. Finally, an optimization solver is applied to optimize object placement based on SDG-guided spatial constraints and physical constraints, including collision avoidance, boundary compliance, and reachability. Both quantitative and qualitative experimental results demonstrate that SDGScenes outperforms state-of-the-art methods in satisfying user intent.

Robotic assembly tasks remain an open challenge due to their long horizon nature and complex part relations. Behavior trees (BTs) are increasingly used in robot task planning for their modularity and flexibility, but creating them manually can be effort-intensive. Large language models (LLMs) have recently been applied to robotic task planning for generating action sequences, yet their ability to generate BTs has not been fully investigated. To this end, we propose LLM-as-BT-Planner, a novel framework that leverages LLMs for BT generation in robotic assembly task planning. Four in-context learning methods are introduced to utilize the natural language processing and inference capabilities of LLMs for producing task plans in BT format, reducing manual effort while ensuring robustness and comprehensibility. Additionally, we evaluate the performance of fine-tuned smaller LLMs on the same tasks. Experiments in both simulated and real-world settings demonstrate that our framework enhances LLMs' ability to generate BTs, improving success rate through in-context learning and supervised fine-tuning.

For robotic manipulation, existing robotics datasets and simulation benchmarks predominantly cater to robot-arm platforms. However, for humanoid robots equipped with dual arms and dexterous hands, simulation tasks and high-quality demonstrations are notably lacking. Bimanual dexterous manipulation is inherently more complex, as it requires coordinated arm movements and hand operations, making autonomous data collection challenging. This paper presents HumanoidGen, an automated task creation and demonstration collection framework that leverages atomic dexterous operations and LLM reasoning to generate relational constraints. Specifically, we provide spatial annotations for both assets and dexterous hands based on the atomic operations, and perform an LLM planner to generate a chain of actionable spatial constraints for arm movements based on object affordances and scenes. To further improve planning ability, we employ a variant of Monte Carlo tree search to enhance LLM reasoning for long-horizon tasks and insufficient annotation. In experiments, we create a novel benchmark with augmented scenarios to evaluate the quality of the collected data. The results show that the performance of the 2D and 3D diffusion policies can scale with the generated dataset. Project page is https://openhumanoidgen.github.io.

The rising demand for customized products necessitates the integration of multiple robotic systems, underscoring the need for advanced production planning and scheduling. This paper introduces an ontology-based, artificial intelligence-enhanced method for dynamic task planning and scheduling, aimed at improving the efficiency of production process, reducing machine downtime, and consequently increasing throughput in assembly operations. Designed to generate and execute feasible production plans dynamically, this method minimizes manual planning and scheduling efforts. We evaluate its effectiveness using two gear assembly use cases with various robot skills, highlighting its flexibility in planning and scheduling and its contributions to the evolution of smart manufacturing. The method’s adaptability suggests its applicability across diverse smart factory environments.