Existing motion generation methods based on MoCap data are often limited by data quality and coverage. In this work, we propose a framework that generates diverse, physically feasible full-body human reaching and grasping motions using only brief walking MoCap data. Based on the observation that walking data captures valuable movement patterns transferable across tasks and, on the other hand, the advanced kinematic methods can generate diverse grasping poses, which can then be interpolated into motions to serve as task-specific guidance. Our approach incorporates an active data generation strategy to maximize the utility of the generated motions, along with a local feature alignment mechanism that transfers natural movement patterns from walking data to enhance both the success rate and naturalness of the synthesized motions. By combining the fidelity and stability of natural walking with the flexibility and generalizability of task-specific generated data, our method demonstrates strong performance and robust adaptability in diverse scenes and with unseen objects.

Traditional bus systems with fixed routes and timetables struggle to accommodate dynamic and diverse passenger demands. Demand-Responsive Transit (DRT) offers a flexible solution through dynamic route planning. However, multi-route cooperative scheduling faces challenges such as high combinatorial optimization complexity and insufficient real-time responsiveness. We propose a Multi-Agent Deep Reinforcement Learning framework for cooperative optimization in dynamic multi-route DRT scheduling (MARL-DRT). The problem is modeled as a multi-agent Markov Decision Process (MDP) aimed at minimizing a weighted total cost, including operating costs, passenger waiting costs, trip cancellations, and real-time demand profit. We employ the Multi-Actor-Attention-Critic (MAAC) algorithm to solve the problem, where each agent dynamically generates station sequences through a policy network based on an encoder-decoder structure. A centralized critic network and the policy gradient method are used to improve global cooperation and scheduling stability. Extensive experiments on real-world and benchmark networks demonstrate that our algorithm outperforms baseline methods in total cost, responsiveness, and service quality, providing a more efficient DRT system with lower operational costs and higher passenger satisfaction.