The LION net comprises two modules: 1) a domain transfer module that bridges the gap between the simulator and the real world and an RL-based control module that utilizes images as input to learning a task. It is expected to be a realistic solution for real-world robot LfO. ), which learns action from image-only demonstrations, e.g., a video of a human demonstrating a task, and reduces the number of the robot–environment interaction to zero in the real world. Hence, this article proposes the LION net ( Although significant progress has been made, the interaction is still inevitable. Therefore, reducing the number of interactions in LfO during training in the real world remains a hot research topic. While in the real world, constrained by the expensive and potentially dangerous interaction between the real robot and the environment, LfO is still challenging to be popularized. Learning from observation (LfO) prompts the robot to imitate actions from experts' states via deep reinforcement learning (RL), achieving satisfactory results in simulation environments through hundreds of thousands of robot–environment interactions. The results suggest the utility of the proposed pipeline for multimodal LfO. We test the pipeline on a real robot and show that the user can successfully teach multiple operations from multimodal demonstrations. In addition, the recognition is made robust through interactions with the user. The pipeline recognizes tasks based on step-by-step verbal instructions accompanied by demonstrations. For input, an user temporally stops hand movements to match the granularity of human instructions with the granularity of robot execution. This paper aims to propose a practical pipeline for multimodal LfO. To the best of our knowledge, however, few solutions have been proposed for LfO that utilizes verbal instruction, namely multimodal LfO. While most previous LfO systems run with visual demonstration, recent research on robot teaching has shown the effectiveness of verbal instruction in making recognition robust and teaching interactive. Learning-from-Observation (LfO) is a robot teaching framework for programming operations through few-shots human demonstration. Our results provide promising directions for incorporating common sense in the literature of robot teaching. Finally, we conduct a preliminary experiment using textual input to explore the possibilities of combining verbal and visual input for recognizing the task groups. We further categorize the frequently appearing constraint patterns into physical, semantic, and multistage task groups and verify that these groups are not only necessary but a sufficient set for covering standard household actions. We then apply our constraint representation to analyze various actions in top hit household YouTube videos and real home cooking recordings. We thoroughly investigate the necessary and sufficient set of contact state and state transitions to understand the different types of physical and semantic constraints. The semantic constraints are represented similar to the physical constraints by defining a contact with an imaginary semantic environment. In order to extend this paradigm to the household domain which consists non-observable constraints derived from a human's common sense we introduce the idea of semantic constraints. Previous research in LfO have mainly focused on the industrial domain which only consist of the observable physical constraints between a manipulating tool and the robot's working environment. The paradigm of learning-from-observation (LfO) enables a robot to learn how to perform actions by observing human-demonstrated actions.
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