Eye-hand coordinated interaction is becoming a mainstream interaction modality in Virtual Reality (VR) user interfaces. Current paradigms for this multimodal interaction require users to learn predefined gestures and memorize multiple gesture-task associations, which can be summarized as an "Operation-to-Intent" paradigm. This paradigm increases users' learning costs and has low interaction error tolerance. In this paper, we propose SIAgent, a novel "Intent-to-Operation" framework allowing users to express interaction intents through natural eye-hand motions based on common sense and habits. Our system features two main components: (1) intent recognition that translates spatial interaction data into natural language and infers user intent, and (2) agent-based execution that generates an agent to execute corresponding tasks. This eliminates the need for gesture memorization and accommodates individual motion preferences with high error tolerance. We conduct two user studies across over 60 interaction tasks, comparing our method with two "Operation-to-Intent" techniques. Results show our method achieves higher intent recognition accuracy than gaze + pinch interaction (97.2% versus 93.1%) while reducing arm fatigue and improving usability, and user preference. Another study verifies the function of eye gaze and hand motion channels in intent recognition. Our work offers valuable insights into enhancing VR interaction intelligence through intent-driven design.

Training and deploying reinforcement learning (RL) policies for robots is a complex task, requiring careful design of reward functions, sim-to-real transfer, and performance evaluation across various robot configurations. These tasks traditionally demand significant human expertise and effort. To address these challenges, this paper introduces Anybipe, a novel, fully automated, end-to-end framework for training and deploying bipedal robots, leveraging large language models (LLMs) for reward function generation, while supervising model training, evaluation, and deployment. The framework integrates comprehensive quantitative metrics to assess policy performance, deployment effectiveness, and safety. Additionally, it allows users to incorporate prior knowledge and preferences, improving the accuracy and alignment of generated policies with expectations. We demonstrate how Anybipe reduces human labor while maintaining high levels of accuracy and safety, examined on three different bipedal robots, showcasing its potential for autonomous RL training and deployment.