Training Humans to Train Robots Dynamic Motor Skills
Learning from demonstration (LfD) is commonly considered to be a natural and intuitive way to allow novice users to teach motor skills to robots. However, it is important to acknowledge that the effectiveness of LfD is heavily dependent on the quality of teaching, something that may not be assured with novices. It remains an open question as to the most effective way of guiding demonstrators to produce informative demonstrations beyond ad hoc advice for specific teaching tasks. To this end, this paper investigates the use of machine teaching to derive an index for determining the quality of demonstrations and evaluates its use in guiding and training novices to become better teachers. Experiments with a simple learner robot suggest that guidance and training of teachers through the proposed approach can lead to up to 66.5% decrease in error in the learnt skill.
Code (0)
등록된 구현이 없습니다.
Tasks
Open-Ended Question AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control
The development of intelligent robots requires control policies that can handle dynamic environments and evolving tasks. Pre-training reinforcement learning has emerged as an effective approach to address these demands b…
Hierarchical Reinforcement Learningreinforcement-learningReinforcement LearningLearning agile and dynamic motor skills for legged robots
Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning,…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Control Policies for Fall prevention and safety in bipedal locomotion
The ability to recover from an unexpected external perturbation is a fundamental motor skill in bipedal locomotion. An effective response includes the ability to not just recover balance and maintain stability but also t…
Deep Reinforcement LearningSensorimotor representation learning for an "active self" in robots: A model survey
Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstruct…
Representation Learning3D Neural Scene Representations for Visuomotor Control
Humans have a strong intuitive understanding of the 3D environment around us. The mental model of the physics in our brain applies to objects of different materials and enables us to perform a wide range of manipulation …
Contrastive LearningFuture predictionNeRFNovel View Synthesis