Designing affective behaviors for animal-inspired social robots often relies on intuition and personal experience, leading to fragmented outcomes. To provide more systematic guidance, we first coded and analyzed human–pet interaction videos, validated insights through literature and interviews, and created structured reference cards that map the design space of pet-inspired affective interactions. Building on this, we developed MojiKit, a toolkit combining reference cards, a zoomorphic robot prototype (MomoBot), and a behavior control studio. We evaluated MojiKit in co-creation workshops with 18 participants, finding that MojiKit helped them design 35 affective interaction patterns beyond their own pet experiences, while the code-free studio lowered the technical barrier and enhanced creative agency. Our contributions include the data-informed structured resource for pet-inspired affective HRI design, an integrated toolkit that bridges reference materials with hands-on prototyping, and empirical evidence showing how MojiKit empowers users to systematically create richer, more diverse affective robot behaviors.

Urban road traffic systems are advancing into sophisticated networks, underscoring the importance of real-time collaborative decision-making. This study tackles the intricate challenge of cooperative path planning under complex urban conditions, taking into account a variety of vehicle types and their respective priorities. While conventional path planning techniques struggle with such intricate coordination, reinforcement learning, though theoretically capable, is hindered by its limited model reusability and protracted training times. To address these issues, we present a novel parallel multi-agent reinforcement learning strategy for path planning that is adaptable to various vehicle types. The problem is initially cast as a multi-agent Markov Decision Process (MDP), followed by the introduction of a parallel training approach within the Q-learning framework. This approach leverages tensor computation to transform the Q-table, state, and reward, thereby markedly accelerating the training process. Empirical simulations demonstrate the approach’s efficacy, achieving a 0.84% reduction in training time (from approximately 771.611 seconds to 0.654 seconds), achieving a 93.94% lower probability of path overlap though the total distance increased by 7.69%.