RHINO: Learning Real-Time Humanoid-Human-Object Interaction from Human Demonstrations

Figure 1: RHINO has the capabilities of real-time interaction on diverse tasks. Abstract--Humanoid robots have shown success in locomotion its effectiveness, flexibility, and safety in various scenarios. We summarize related works in each tasks in real-time. Some others focus on recognizing human category and highlight the differences between our work. The robot cannot be interrupted once a task Humanoid robots need to estimate the human physical and is in progress, and further human commands can only be mental states to provide appropriate assistance [35]. Object information in the complexity of human interactions [33, 37], but they often the environment also plays an important role in predicting suffer from high latency and are not suitable for real-time human intention by combining it with human motion. These limitations hinder robots from rapid interaction, such as pointing gestures [14] and grabbing interventions and robust, multi-step interactions in humancentered objects [24], provides a broader semantic space for human tasks. Most works on human intention recognition treat human-robot interaction with real-time intention recognition the interaction as a two-stage process, where the robot first and various skills is urgently needed to tackle the above predicts the human intention and then executes the task. Our work aims to react learning framework for Reactive Humanoid-human to human signals in real time, enabling the downstream tasks INteraction and Object Manipulation. RHINO decouples the to be interrupted at any time. In human-robot interaction and object manipulation skills based on predicted intentions. To ensure unique opportunity to learn natural motion from retargeted the scalability of RHINO across a wide range of skills, we human motion data [15]. Human motion data can be collected design a pipeline for learning the interactions from humanobject-human from motion capture systems or network videos.