Conventional metamaterial inverse design is hindered by stochastic generative models that lack physical grounding and often fail to reach optimal performance. Here, we introduce a framework coupling a physics‐informed neural operator (DeepONet) and a 3D geometry generator (3D‐cVAE) with a Directional Latent Hybridization (DLH) strategy. By merging dominant traits from parent geometries in the latent space, our approach enables deterministic latent optimization, yielding a high prediction accuracy for energy (
R
2
= 0.957) and force (
R
2
= 0.981). Unlike random noise‐based generation, which suffers from a 0% success rate at high volume fractions (
V
r
= 0.8), the DLH strategy maintains a 73% success rate and achieves significantly lower volume errors (< 4.81%). Experimental validation using additively manufactured thermoplastic polyurethane (TPU) lattices confirms that DLH‐optimized architectures exceed the performance of their base designs, achieving up to 24.03 J of absorbed energy and a peak force of 9.80 kN. This framework establishes a novel physics‐informed generative paradigm for discovering next‐generation metamaterials with physically interpretable and predictable performance.
Voice interaction has been actively studied in human–robot interaction (HRI) for decades, yet deploying spoken interfaces on physical mobile manipulators remains challenging because language is ambiguous, tasks are long-horizon, and robot actions must be grounded to perception and motion under real-world uncertainties. Recent large language models (LLMs) offer a practical way to interpret open-ended spoken requests, but their non-deterministic outputs and limited transparency can hinder safe and reproducible robot execution. This paper presents an LLM-based Voice-to-Action (VTA) system that converts spoken user commands into grounded robot behaviors for an indoor mobile manipulator, LeeAhn 2. The system combines speech transcription with an LLM that produces structured, skill-level action plans aligned with a predefined library of robot capabilities, including vision-based seeking, wheeled navigation, and manipulation. To improve reliability, we incorporate interface constraints that restrict generated actions to executable skills and enable recovery from common failures during execution. We evaluate the proposed system through simulation experiments and real-world trials on a representative search task, reporting component-level performance across seeking, navigation, and manipulation. The simulation experiments provide repeatable analysis under controlled conditions, while the real-world trials demonstrate practical applicability on the LeeAhn 2 mobile manipulator and reveal limitations such as latency and occasional plan/execution failures. Our results suggest that LLM-grounded spoken interfaces can reduce operator burden and improve accessibility for indoor service robots.
Kisu Ok, Geunyoung Heo, Cheonghwa Lee et al.· International Journal of Pre...· 1 citation