Assistive Teaching of Motor Control Tasks to Humans

• URL: http://arxiv.org/abs/2211.14003v1
• Date: Fri, 25 Nov 2022 10:18:29 GMT
• Title: Assistive Teaching of Motor Control Tasks to Humans
• Authors: Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah Goodman, Dorsa Sadigh
• Abstract summary: We propose an AI-assisted teaching algorithm that breaks down any motor control task into teachable skills. We show that assisted teaching with skills improves student performance by around 40% compared to practicing full trajectories without skills.
• Score: 18.537539158464213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent works on shared autonomy and assistive-AI technologies, such as assistive robot teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be desirable for a human to intervene, these methods may inhibit their ability to learn how to succeed with full self-control. In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft. Despite their ubiquitous role in humans' daily activities and occupations, motor tasks are rarely taught in a uniform way due to their high complexity and variance. We propose an AI-assisted teaching algorithm that leverages skill discovery methods from reinforcement learning (RL) to (i) break down any motor control task into teachable skills, (ii) construct novel drill sequences, and (iii) individualize curricula to students with different capabilities. Through an extensive mix of synthetic and user studies on two motor control tasks -- parking a car with a joystick and writing characters from the Balinese alphabet -- we show that assisted teaching with skills improves student performance by around 40% compared to practicing full trajectories without skills, and practicing with individualized drills can result in up to 25% further improvement. Our source code is available at https://github.com/Stanford-ILIAD/teaching

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)
• 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)
• Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment [52.07473934146584]
  We guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process. Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications. It shows reinforcement learning performance can successfully adjust in sync with the human desired difficulty level.
  arXiv  Detail & Related papers  (2022-08-04T23:53:51Z)
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
  Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems. We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
  arXiv  Detail & Related papers  (2021-09-19T18:00:51Z)
• Hierarchical Affordance Discovery using Intrinsic Motivation [69.9674326582747]
  We propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties.
  arXiv  Detail & Related papers  (2020-09-23T07:18:21Z)
• Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion [19.285099263193622]
  We develop a learning framework that can learn sophisticated locomotion behavior for a wide spectrum of legged robots. Our learning framework relies on a data-efficient, off-policy multi-task RL algorithm and a small set of reward functions that are semantically identical across robots. For nine different types of robots, including a real-world quadruped robot, we demonstrate that the same algorithm can rapidly learn diverse and reusable locomotion skills.
  arXiv  Detail & Related papers  (2020-08-06T08:23:55Z)
• Learning Agile Robotic Locomotion Skills by Imitating Animals [72.36395376558984]
  Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. We present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
  arXiv  Detail & Related papers  (2020-04-02T02:56:16Z)
• Learning to Walk in the Real World with Minimal Human Effort [80.7342153519654]
  We develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort. Our system can automatically and efficiently learn locomotion skills on a Minitaur robot with little human intervention.
  arXiv  Detail & Related papers  (2020-02-20T03:36:39Z)

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.