iNDICA NEWS BUREAU-
An Indian-origin researcher at Arizona State University is using AI to develop intelligent robots that can complete complex tasks. The researcher and his team are developing an AI toolkit that would enable robots to perform miscellaneous tasks including domestic chores that are often considered unwanted.
Siddharth Srivastava, an associate professor of computer science and engineering in the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at Arizona State University, and also heads up the Autonomous Agents and Intelligent Robots Lab, or AAIR Lab, and his team of doctoral students are studying how to develop intelligent robots that can complete complex tasks under uncertain conditions. “We must find ways to get robots to do those jobs that are dirty, dull, or dangerous,” Srivastava says. “We want them to do the kinds of jobs people would prefer to avoid.”
Funded by multiple grants from the National Science Foundation, Srivastava and his team will create AI solutions in robotics. The resulting toolkit will combine new algorithms, or sets of instructions that robots use to do their work, with language data and real-world models. These algorithms will enable a robot to use its own experiences to invent reusable, high-level actions. It can then formulate new plans for solving more complex tasks without more input from a user.
Srivastava explained that robots have been capable of helpful and advanced activities for a long time. He added that robots are used to help assemble cars in automotive factories and make operations easier and faster at companies like Amazon. “But those systems require expert-level coding and hand-engineering,” Srivastava says. “Robots must be preprogrammed with exact information about the task that needs to be completed, how it should be completed, and the kind of space the robot is operating in.” The need to tell a robot how to weld two car parts together or where a pallet of books can be found in an Amazon warehouse makes developing and deploying these systems difficult and expensive, he added. “The current development process keeps robots out of many settings where their use would be desirable.”
Srivastava considers the challenge of giving a robot high-level instructions, which are a set of directions in simple, plain language. “If I ask a robot to bring me coffee, I don’t want it to show up with coffee beans,” Srivastava says. “I don’t want it to run into my family. And I don’t want it to set my house on fire in the process of brewing my drink.”
In the AAIR Lab, the team members are testing their work using a Fetch mobile manipulator robot from computing hardware and software company Zebra that they have named Alfred. Currently, Alfred is learning to clear the dishes after a meal. Alfred was given a demonstration on how to pick up and place down a cup and a bowl.
Through algorithms, Srivastava and his team want Alfred to develop its own logical theory of the world, coming to understand the reusable actions required to repeatedly move the dishes. The process allows the robot to autonomously create knowledge that would otherwise need to be hand-coded by experts. With that knowledge in place, Alfred makes its own decisions on what route to take, how to move its arms and where to place the dishes.
“The task of setting a table with hand-coded primitives has been done before,” Srivastava says. “The main technical innovation here is that the robot learns on its own how to generalize.” This new process will accomplish two things – it will allow the robot to operate in any area, without an engineer preprogramming information about the space and, it will make the deployment of robots substantially less expensive and less time-consuming as it will reduce the amount of manual programming work required.
Srivastava and his team see many possible applications of this new technology and are looking at uses in the medical sector, including the possibility of robots that can clean hospital rooms. Since the robot could learn on its own to recognize different room configurations and develop a plan to clean the area, the team’s technology has the potential to improve hospital operations. The researcher also believes that the toolkit could be of great use in disaster-recovery support systems where it is impossible to know in advance what kind of conditions might be encountered.
