The rapid adoption of autonomous multicopters in low-altitude airspace has necessitated new noise mitigation strategies. As mechanical noise reduction reaches its physical limits, trajectory optimization via intelligent agents offers a complementary solution. However, training such agents, specifically via Reinforcement Learning, is hindered by the lack of simulation environments that couple rigid-body dynamics with acoustic emission models. In this paper, we present an online acoustic simulation framework for multicopters. We introduce a parametric acoustic model that operates entirely in the frequency domain, bypassing the computational overhead of Fast Fourier Transforms (FFT). It accounts for propeller speed, flight state, and environmental factors, integrated within the Robot Operating System 2 (ROS 2) with an interface to the PX4-Autopilot Software-in-the-Loop (SITL). We validate the model by comparing simulated observer signals with real-world acoustic flight data. The results show low errors in simulated overflights for A-weighted spectra ranging between 2.5% and 9%. This validation demonstrates the viability of our modeling approach as a tool for training acoustic-aware autonomous multicopter agents.
Recent discussions on the Ethical, Legal, and Social Issues (ELSI) of social robots highlight the importance of understanding the ethical nature of human-robot relationships beyond issues of safety and regulation. Drawing on the philosophy of technology-from Ivan Illich's concept of convivial tools to contemporary relational approaches-this paper proposes a conceptual framework for human-robot coexistence based on two complementary paradigms: convivial robotics and silicopathy. Convivial robotics, inspired by Illich's notion of conviviality and exemplified by the “weak robot” approach, emphasizes robots that enhance human capabilities through imperfect yet mutually supportive interaction. Silicopathy, by contrast, refers to the emergence of artificial empathy grounded in representations of pain and affective states, through which ethical behavior is generated from internally constructed value dynamics, enabling morally meaningful relationships between humans and robots. Together these paradigms form a dual ethical framework that integrates human capability enhancement with empathic moral relations. The framework is further discussed from a cross-cultural perspective, contrasting Western autonomy-centered ethics with Eastern relational philosophies emphasizing harmony and compassion, thereby offering a conceptual basis for future human-robot coexistence, including its governance across diverse cultural and societal contexts.
Creating social robots is a multidisciplinary challenge that requires understanding human perception, behavior interpretation, and social–ethical expectations in human–robot interaction. Animal-like robots are a key category in this domain, yet quantitative evidence on user acceptance and perceived safety, especially for quadruped robots, remains limited. We therefore conducted a multilingual survey on the perception and acceptance of quadruped robots with $\mathbf{N} \boldsymbol{=} \mathbf{4 5 4}$ participants. Importantly, participants formed their judgments from indirect exposure to standardized visual stimuli (images and short videos) rather than from direct physical interaction. The results suggest that reported comfort under indirect exposure is associated with robot appearance and the coherence of design cues across animal-like and machine-like constructions, with mismatched zoomorphic cues tending to be viewed less favorably. Concerns about job loss and accidents are negatively associated with perceived safety, while concerns about unfair decisions and privacy show no significant influence. Prior experience with robotics is associated with higher acceptance and reduced anxiety. Appearance shows up to support initial sense-making, but did not correspond to uniformly high reported trust; responsibility attribution in the event of malfunction also remained contested, with responses clustering into technology-centered, human-centered, and shared-responsibility positions. Overall, the findings provide exploratory quantitative evidence on mediated first impressions of quadruped robots and motivate follow-up studies using direct human–robot interaction to test, validate, and extend these observations.
Service robots must reliably execute natural-language instructions in dynamic, partially observable environments, yet existing language-driven planners lack verifiability, adaptability, and computational efficiency. To address these challenges, we propose HSP-Plan, a hierarchical and verifiable planning framework that combines high-level LLM-based task decomposition with symbolic planning (via PDDL), efficient sub-goal execution using a smaller language model, and a scene graph-based memory capturing spatial and temporal relations for adaptive replanning. In ALFWorld, HSP-Plan achieves a task success rate of 0.78, outperforming AutoGen (0.77) and significantly surpassing ReAct (0.55). It further improves sub-goal completion to 0.88 and reduces high-cost LLM calls to approximately 6 per episode, compared to about 18 for ChatGPT-only execution. These results demonstrate that integrating hierarchical reasoning, symbolic verification, and structured memory enables more reliable and efficient language-guided robot planning in complex environments.
This paper presents UNCOM, a novel hybrid framework for interpreting natural human commands in tabletop scenarios. The system integrates multiple sources of information - speech, gestures, and scene context - to extract structured, actionable instructions for robots. Addressing the need for general-purpose human-robot interaction in domestic environments, UNCOM is designed for zero-shot operation, without reliance on predefined object models or training data specific to a given task. Using foundational and task-specific deep learning models, it allows out-of-the-box speech recognition, natural language understanding, gesture detection, and object segmentation. The modular architecture enhances transparency and explainability by explicitly parsing commands into object-action-target representations, enabling integration with symbolic robotic frameworks. We demonstrate the system in a TIAGo++ robot and provide an evaluation on a real-world data set of human-robot interaction scenarios; achieving an 82.39% success rate over our benchmark data set, highlighting the robustness of the system to diversity, noise, and communication ambiguity. The data set, evaluation scenarios, and the code are publicly available to support future research.
