Xiem HoangVan

Thang Tran Viet, Thanh Nguyen Canh, Gia Huy Uong et al.

Expressive co-speech gestures are crucial for natural human-robot interaction, but generating them on physical humanoid robots is difficult because gesture strokes must align with speech emphasis while satisfying strict kinematic and dynamic constraints. Unlike virtual avatars, humanoid robots cannot freely execute rapid or overlapping motions, making word-level synchronization and hardware-safe motion planning a coupled problem. We present \textbf{WaveSync}, a hybrid framework in which a Large Language Model decomposes dialogue responses into structured semantic schemas and assigns per-word importance weights, constructing a continuous Semantic Importance Wave. Gesture trajectories are shaped through Dynamic Movement Primitives, enforcing kinematic feasibility while enhancing expressiveness. A Wavefront Optimization stage aligns peak-to-peak gesture-speech synchronization and resolves residual kinematic violations through gesture-duration compression and forward propagation. Experimental evaluation based on five dialogue scenarios shows that our method achieves high synchronization accuracy and outperforms three baselines in both objective and subjective evaluations. Each component in WaveSync plays a necessary role in producing gestures that are expressive, semantically grounded, and kinematically compliant. The code, resources, and videos are available at \href{https://github.com/pairs-lab/WaveSync}{WaveSync}

Thanh Nguyen Canh, Thanh-Tuan Tran, Haolan Zhang et al.

Translating video demonstrations into executable robot commands remains challenging because existing methods often fail to identify which objects are functionally involved in the demonstrated action. As a result, they may generate commands that are linguistically plausible but operationally ambiguous. We propose an object-centric video understanding framework that decouples action recognition from object identification to generate precise, grammar-free manipulation commands. Our approach integrates Temporal Shift Modules (TSM) for efficient spatio-temporal action classification with a novel \textbf{Object Selection} algorithm that identifies task-relevant objects through trajectory-based role classification, blur detection, and overlap minimization. The selected objects are then processed by Vision-Language Models (VLMs) for robust category recognition and zero-shot generalization. Evaluated on a modified Something-Something V2 dataset, our method achieves 86.79\% action classification accuracy and BLEU-4 scores of 0.337 on standard objects and 0.261 on novel objects. These results improve over the strongest task-specific baseline by 80.2\% and 143.9\%, respectively. Larger gains are observed in METEOR and CIDEr, reaching 157.9\% and 171.7\% on novel objects. Across all semantic metrics, our approach consistently outperforms task-specific methods and remains competitive with, or surpasses, large general-purpose VLMs while retaining a modular, object-centric design.

Thanh Nguyen Canh, Thang Tran Viet, Phuc Van Dinh et al.

Coordinating heterogeneous multi-robot systems (MRS) for complex, long-horizon tasks requires both flexible high-level reasoning and efficient low-level scheduling. Existing LLM-based approaches address the reasoning side but introduce two critical bottlenecks: (1) repeated LLM inference during execution, which inflates latency with agent count, and (2) offline, pre-committed scheduling, which forces robots to idle while waiting for sequentially ordered predecessors even when independent work is available. This paper presents OSDAG, a novel framework that integrates LLM-based task reasoning with Directed Acyclic Graph (DAG) representation and constraint-aware online scheduling. The LLM is invoked once to decompose a natural-language instruction into a dependency-annotated task graph, and a lightweight online scheduler then allocates ready tasks to idle agents in real time. The DAG representation encodes both precedence and resource constraints, ensuring correctness while exposing all available parallelism. Experiments across five benchmark scenarios demonstrate that OSDAG achieves 5-15x faster reasoning time compared to dialogue-based methods, reduces makespan by up to 38% over sequential baselines, and maintains competitive success rates. Both simulation and real-world experiments on dual-arm manipulation tasks validate the effectiveness and practicality of the proposed approach for efficient multi-robot coordination. The website and resources are available at http://thanhnguyencanh.github.io/LLM_DAG4MultiRobot