Human insights inspire solutions for household robots

05 February 2015

share this:
Credit: Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell.

Credit: Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell.

People typically consider doing the laundry to be a boring chore. But laundry is far from boring for artificial intelligence (AI) researchers like Siddharth Srivastava, a scientist at the United Technologies Research Center, Berkeley.

To AI experts, programming a robot to do the laundry represents a challenging planning problem because current sensing and manipulation technology is not good enough to identify precisely the number of clothing pieces that are in a pile and the number that are picked up with each grasp. People can easily cope with this type of uncertainty and come up with a simple plan. But roboticists for decades have struggled to design an autonomous system able to do what we do so casually — clean our clothes.

In work done at the University of California, Berkeley, and presented at the Association for Advancement of Artificial Intelligence conference in Austin, Srivastava (working with Abhishek Gupta, Pieter Abbeel and Stuart Russell from UC Berkeley and Shlomo Zilberstein from University of Massachusetts, Amherst) demonstrated a robot that is capable of doing laundry without any specific knowledge of what it has to wash.

The video shows the PR2 doing laundry using the approach presented in the paper “Tractability of Planning with Loops” by Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell, in the proceedings of the 29th Association for the Advancement of Artificial Intelligence (AAAI-15).Credit: Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell

Earlier work by Abbeel’s group had demonstrated solutions for the sorting and folding of clothes. The laundry task serves as an example for a wide-range of daily tasks that we do without thinking but that have, until now, proved difficult for automated tools assisting humans.

“The widely imagined helper robots of the future are expected to ‘clear the table,’ ‘do laundry’ or perform day-to-day tasks with ease,” Srivastava said. “Currently however, computing the required behavior for such tasks is a challenging problem — particularly when there’s uncertainty in resource or object quantities.”

Humans, on the other hand, solve such problems with barely a conscious effort. In their work, the researchers showed how to compute correct solutions to problems by using some assumptions about the uncertainty.

“The main issue is how to develop what we call ‘generalized plans,'” said Zilberstein, a professor of computer science and director of the Resource Bound Reasoning Lab at UMass Amherst. “These are plans that don’t just work in a particular situation that is very well defined and gets you to a particular goal that is also well defined, but rather ones that work on a whole range of situations and you may not even know certain things about it.”

The researchers’ key insight was to use human behavior — the almost unconscious action of pulling, stuffing, folding and piling — as a template, adapting both the repetitive and thoughtful aspects of human problem-solving to handle uncertainty in their computed solutions.

By doing so, they enabled a PR2 robot to do the laundry without knowing how many and what type of clothes needed to be washed.

Out of the 13 or so tasks involved in the laundry problem, the team’s system was able to complete more than half of them autonomously and nearly completed the rest–by far the most effective demonstration of laundering AI to date.

The framework that Srivastava and his team developed combines several popular planning paradigms that have been developed in the past using complex control structures such as loops and branches and optimizes them to run efficiently on modern hardware. It also incorporates an effective approach for computing plans by learning from examples, rather than through rigid instructions or programs.

“What’s particularly exciting is that these methods provide a way forward in a problem that’s well known to be computationally unsolvable in the worst case,” Srivastava said. “We identified a simpler formulation that is solvable and also covers many useful scenarios.”

“It is exciting to see how this breakthrough builds upon NSF-funded efforts tackling a variety of basic-research problems including planning, uncertainty, and task repetition,” said Héctor Muñoz-Avila, program director at NSF’s Robust Intelligence cluster.

Though laundry robots are an impressive, and potentially time-saving, application of AI, the framework that Srivastava and his team developed can be applied to a range of problems. From manufacturing to space exploration to search-and-rescue operations, any situation where artificially intelligent systems must act, despite some degree of uncertainty, can be addressed with their method.

“Using this approach, solutions to high-level planning can be generated automatically,” Srivastava said. “There’s more work to be done in this direction, but eventually we hope such methods will replace tedious and error-prone task-specific programming for robots.”

Siddharth Srivastava
Shlomo Zilberstein

Related Institutions/Organizations
United Technologies Research Center
University of Massachusetts Amherst

Berkeley , California
Amherst , Massachusetts

Related Programs
Robust Intelligence

Related Awards
#0915071 RI: Small: Foundations and Applications of Generalized Planning

Years Research Conducted
2009 – 2015

Total Grants

Related Agencies
Office of Naval Research

tags: , , , , , , , , , ,

the National Science Foundation (NSF) is an independent federal US agency created to promote the progress of science.
the National Science Foundation (NSF) is an independent federal US agency created to promote the progress of science.

Related posts :




Robotics Grasping and Manipulation Competition Spotlight, with Yu Sun

Yu Sun, previous chair of the Robotics Grasping and Manipulation Competition, speaks on the value that this competition brought to the robotics community.
21 May 2022, by



Early Days of ICRA Competitions, with Bill Smart

Bill Smart, one fo the early ICRA Competition Chairs, dives into the high-level decisions involved with creating a meaningful competition.
21 May 2022, by

New imaging method makes tiny robots visible in the body

Microrobots have the potential to revolutionize medicine. Researchers at the Max Planck ETH Centre for Learning Systems have now developed an imaging technique that for the first time recognises cell-​sized microrobots individually and at high resolution in a living organism.
20 May 2022, by

A draft open standard for an Ethical Black Box

Within the RoboTIPS project, we have developed and tested several model of Ethical Black Boxes, including one for an e-puck robot, and another for the MIRO robot.
19 May 2022, by

Unable to attend #ICRA2022 for accessibility issues? Or just curious to see robots?

There are many things that can make it difficult to attend an in person conference in the United States and so the ICRA Organizing Committee, the IEEE Robotics and Automation Society and OhmniLabs would like to help you attend ICRA virtually.
17 May 2022, by



Duckietown Competition Spotlight, with Dr Liam Paull

Dr. Liam Paull, cofounder of the Duckietown competition talks about the only robotics competition where Rubber Duckies are the passengers on an autonomous driving track.
17 May 2022, by

©2021 - ROBOTS Association


©2021 - ROBOTS Association