![]() ![]() And I think that that’s been helping people understand that, because they see that second step. So now we’re trying to show coming out of these challenges are these couple of few breakthroughs that we think are important, and then apply those to real applications. Can you help us?’ And we said, yeah, we have technologies that apply to that. We went to factories and they said, ‘yes, this is a problem. How do you translate that to normal people? Normal people aren’t hung up on challenge tasks.Įxactly, but that’s why in the demonstration you saw today, we tried to show the challenge tasks, but also one example of how you take capabilities that come out of that challenge and apply it to a real application like unloading a container. That’s why Gill says every time, ‘reemphasize why this is a challenge task.’ We know how difficult these challenges are and how far off things are, but some random person sees your video, and suddenly it’s something that’s just over the horizon, even though you can’t deliver that.Ībsolutely. The grocery market is a very good representation because it has that huge diversity. We like the home because it is representative of where we eventually want to be helping people in the home. The DARPA Robotics Challenges, those were just made-up tasks that were hard. To be totally honest, the challenge problem kind of doesn’t matter. Are we able to do the perception, the motion planning, the behaviors that are, in fact, general purpose. Or even just measure if we’re pushing the state of the art in robotics. You’re not talking about specific goals to either the home or supermarket, but solving for problems that can span both of those places. The grocery challenge task where we said, we need an environment where it’s as hard as a home or has the same representative problems as a home, but where we can measure how much progress we’re making. We’re not good enough yet, here’s a cool video.” We didn’t know whether we were making good progress or not. We were doing the standard, “show a demo, show a cool video. Let’s say we were a little better at tidying this one house, we don’t know if that’s because our capabilities got better or if that house was a little easier. Isn’t that ideal that you don’t get the same home every time?Įxactly, but the problem is we couldn’t measure how well we were doing. But if we did, we would overfit to that home. We were deploying into Airbnbs to see how well we were doing, but the problem is we couldn’t get the same home every time. We would put things throughout the house to make the robot tidy. We would put flour and rice on the tables and we would try to wipe them up. It was that it was too hard to measure the progress we were making. The problem with the home is not that it was too hard. We pick challenge tasks because they are hard. One of the things that we learned in that process is that we weren’t able to measure our progress very well. Home was one of our original challenge tasks.Ībsolutely. Max Bajracharya: We are still doing some home robot stuff What we’ve done has shifted. Note that the text has been edited for clarity and length. That was drawn from a conversation with Bajracharya, which we’re printing in a more complete state below. You can read a lengthier writeup of that pivot in an article published on TechCrunch earlier this week. It does, however, represent a pivot away from their original work of building robots designed to execute household tasks like dishwashing and food prep. The system is a direct outgrowth of the 50-person robotics team’s focus on eldercare, aimed at addressing Japan’s aging population. The system is able to extend to the top shelf to find items, before determining the best method for grasping the broad range of different objects and dropping them into its basket. A shopping robot retrieves different products on the shelf based on bar codes and general location. ![]() The other is a bit more surprising - at least for those who haven’t followed the division’s work that closely. First was something more along the lines of what one would expect from Toyota: an industrial arm with a modified gripper designed for the surprisingly complex task of moving boxes from the back of a truck to nearby conveyor belts - something most factories are hoping to automate in the future. SVP Max Bajracharya showcased a pair of projects. Robotics, a longtime focus of Toyota’s research division, were on display, as well. It was a day full of demos, ranging from driving simulators and drifting instructors to conversations around machine learning and sustainability. Earlier this week, the Toyota Research Institute opened the doors of its Bay Area offices to members of the media for the first time. ![]()
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