Training Humans to Train Robots Dynamic Motor Skills

• URL: http://arxiv.org/abs/2104.08631v1
• Date: Sat, 17 Apr 2021 19:39:07 GMT
• Title: Training Humans to Train Robots Dynamic Motor Skills
• Authors: Marina Y. Aoyama, Matthew Howard
• Abstract summary: This paper investigates the use of machine teaching to derive an index for determining the quality of demonstrations. 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.
• Score: 5.5586788751870175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation [58.14969377419633]
  We propose spire, a system that decomposes tasks into smaller learning subproblems and second combines imitation and reinforcement learning to maximize their strengths. We find that spire outperforms prior approaches that integrate imitation learning, reinforcement learning, and planning by 35% to 50% in average task performance.
  arXiv  Detail & Related papers  (2024-10-23T17:42:07Z)
• Effects of Multimodal Explanations for Autonomous Driving on Driving Performance, Cognitive Load, Expertise, Confidence, and Trust [2.9143343479274675]
  We tested the impact of an AI Coach's explanatory communications modeled after performance driving expert instructions. Results show AI coaching can effectively teach performance driving skills to novices. We suggest efficient, modality-appropriate explanations should be opted for when designing effective HMI communications.
  arXiv  Detail & Related papers  (2024-01-08T19:33:57Z)
• How Can Everyday Users Efficiently Teach Robots by Demonstrations? [3.6145826787059643]
  We propose to use a measure of uncertainty, namely task-related information entropy, as a criterion for suggesting informative demonstration examples to human teachers. The results indicated a substantial improvement in robot learning efficiency from the teacher's demonstrations.
  arXiv  Detail & Related papers  (2023-10-19T18:21:39Z)
• Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition [3.07176124710244]
  Motor skills, especially fine motor skills like handwriting, play an essential role in academic pursuits and everyday life. Traditional methods to teach these skills, although effective, can be time-consuming and inconsistent. We introduce an AI teacher model that captures the distinct characteristics of human instructors.
  arXiv  Detail & Related papers  (2023-10-16T11:11:43Z)
• Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
  We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher. We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-03-19T03:22:57Z)
• Iterative Teacher-Aware Learning [136.05341445369265]
  In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
  arXiv  Detail & Related papers  (2021-10-01T00:27:47Z)
• Learning to Teach with Student Feedback [67.41261090761834]
  Interactive Knowledge Distillation (IKD) allows the teacher to learn to teach from the feedback of the student. IKD trains the teacher model to generate specific soft target at each training step for a certain student. Joint optimization for both teacher and student is achieved by two iterative steps.
  arXiv  Detail & Related papers  (2021-09-10T03:01:01Z)
• Adversarial Training is Not Ready for Robot Learning [55.493354071227174]
  Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations. We show theoretically and experimentally that neural controllers obtained via adversarial training are subjected to three types of defects. Our results suggest that adversarial training is not yet ready for robot learning.
  arXiv  Detail & Related papers  (2021-03-15T07:51:31Z)
• Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners [26.006964607579004]
  We focus on a common reinforcement learning method, Q-learning, and examine what assumptions people have using a behavioral experiment. We use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states. Our results reveal how people teach using evaluative feedback and provide guidance for how engineers should design machine agents in a manner that is intuitive for people.
  arXiv  Detail & Related papers  (2020-09-05T06:32:38Z)
• Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation [16.28164706104047]
  We develop a novel preference-aware assistance knowledge learning approach. An assistance preference model learns what assistance is preferred by a human. We also develop knowledge transfer methods to transfer the preference knowledge across different robot hand structures.
  arXiv  Detail & Related papers  (2020-03-07T04:49:57Z)
• Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience [76.9910678786031]
  We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting. Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
  arXiv  Detail & Related papers  (2020-01-24T22:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.