Methods for learning from demonstration (LfD) have shown success in acquiring behavior policies by imitating a user. However, even for a single task, LfD may require numerous demonstrations. For versatile agents that must learn many tasks via demonstration, this process would substantially burden the user if each task were learned in isolation. To address this challenge, we introduce the novel problem of lifelong learning from demonstration, which allows the agent to continually build upon knowledge learned from previously demonstrated tasks to accelerate the learning of new tasks, reducing the amount of demonstrations required. As one solution to this problem, we propose the first lifelong learning approach to inverse reinforcement learning, which learns consecutive tasks via demonstration, continually transferring knowledge between tasks to improve performance.
Teaching a robot how to do something is usually done by either programming it to perform a specific task, or demonstrating that task for the robot to observe and imitate. The latter method, however, so far hasn't been accurate enough for robots to be able to transfer their knowledge to other robots. That's changing, however, thanks to researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and a their new teaching method, called C-LEARN. That could have far-reaching consequences by making it easier for non-programmers to teach robots how to perform certain tasks. Even better, it allows robots to teach other robots how to perform the same tasks.
Myers, Karen (SRI International) | Kolojejchic, Jake (General Dynamics C4 Systems | Viz) | Angiolillo, Carl (General Dynamics C4 Systems | Viz) | Cummings, Tim (General Dynamics C4 Systems | Viz) | Garvey, Tom (SRI International) | Gaston, Matt (Carnegie Mellon University) | Gervasio, Melinda (SRI International) | Haines, Will (SRI International) | Jones, Chris (SRI International) | Keifer, Kellie (SRI International) | Knittel, Janette (General Dynamics C4 Systems | Viz) | Morley, David (SRI International) | Ommert, William (General Dynamics C4 Systems | Viz) | Potter, Scott (General Dynamics C4 Systems | Viz)
Learning by demonstration technology has long held the promise to empower non-programmers to customize and extend software. We describe the deployment of a learning by demonstration capability to support user creation of automated procedures in a collaborative planning environment that is used widely by the U.S. Army. This technology, which has been in operational use since the summer of 2010, has helped to reduce user workloads by automating repetitive and time-consuming tasks. The technology has also provided the unexpected benefit of enabling standardization of products and processes.
In an important advance that takes us one step closer to the inevitable robopocalypse, MIT researchers have developed a system that teaches robots how to acquire new skills--and then teach those skills to different types of robots. The system is called C-LEARN, and it was developed by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). Using C-LEARN, people who have no experience with computer programming can teach a robot how to perform a task--like dropping a flask into a bucket, or pulling a rod from a container--by providing it with some basic rules about the task, and allowing the robot to view a single demonstration of the task being completed. Incredibly, a robot can then transfer this newly-acquired knowledge to another robot, even if the robot learning is physically different than the robot teaching. Eventually, the C-LEARN system could allow factories to utilize a host of different robot types, and not have to worry about programming each and every one of them individually.
Programming by demonstration systems have long attempted to make it possible for people to program computers without writing code. However, while these systems have resulted in many publications in AI venues, none of the technologies have yet achieved widespread.adoption. Usability remains a critical barrier to their success. On the basis of lessons learned from three different programming by demonstration systems, we present a set of guidelines to consider when designing usable AI-based systems.