This research project addresses the challenge of instructing a robot agent to learn novel tasks interactively, specifically in the household domain. The focus of this repository lies on the task of teaching the pouring task by using written instructions with PyCRAM.
For more information, you can visit the webpage of Interactive Task Learning to get a better idea on how a robot can learn from different teaching methodologies.
Interactive Actions and/or Examples
Description
ITL is an emerging A.I. challenge, defined as “any process by which an agent improves its performance on some task through experience, when [that experience] consists of a series of sensing, effecting, and communicating interactions between (the agent), its world, and crucially other agents in the world(John Leird, Kevin A. Gluck).” An ITL setup is an apprentice-style learning approach where most aspects of the task can be explicitly taught by an instructor and the student can accumulate task-specific knowledge not only from interactions but also from past experiences to solve the novel task execution problem. We investigate the predominant natural interaction methods employed by humans to instruct each other, which include bootstrap task-specific instruction (”telling what to do”) and demonstration (”showing how to do it”).
Example Videos
VR Human task demonstrations
VR Human task demonstrations as NEEM
PR2 Pouring task demonstrations with PyCRAM