Five Things Learned From The DARPA Challenge.

Earlier this year I posted about the DARPA humanoid robot challenge last year.

https://theartsmechanical.wordpress.com/2016/03/25/the-darpa-trials/

Here’s some things that one of the teams learned from the challenge.

WARNER successfully completed seven of eight tasks on each day of the competition, which included driving a vehicle, opening a door, using power tools, and turning a valve. The team scored 14 out of 16 possible points over the two-day event. It suffered a program design error and an arm hardware failure that caused two attempts at the drill/cutting task to fail. However, WPI-CMU was the only team that attempted all tasks, did not require physical human intervention (a reset), and did not fall during any of the missions.

What They Learned

The Institute of Electrical and Electronics Engineers published a paper written by WPI-CMU team members called “No Falls, No Resets: Reliable Humanoid Behavior in the DARPA Robotics Challenge.” They describe their approach to the DRC and their strategy for avoiding failures that required physical human intervention, and lessons learned. Five major takeaways include:

  • Walk with your entire body. All teams in the challenge failed to use aspects of the physical space to help their robots move, such as stair railings, or putting a hand on the wall to help cross rough terrain. “Even drunk people are smart enough to use nearby supports,” they write. “Why didn’t our robots do this? We avoided contacts and the resultant structural changes. More contacts make tasks mechanically easier, but algorithmically more complicated.”
  • Design robots to survive failure and recover. The authors advise that robustness to falls (avoiding damage) and fall recovery (getting back up) need to be designed in from the start, not retro-fitted to a completed humanoid design. The Atlas robot was too top heavy and its arms too weak to reliably get up from a fall. The team has since been exploring inflatable robot designs as one approach to fall-tolerant robots.
  • The most cost-effective research area to improve robot performance is human-robot interaction.Developing ways to avoid and survive operator errors is crucial for real-world robotics. Interfaces must be designed to help eliminate errors, reduce the effect of errors, and speed the recovery or implement “undo” operations when errors happen. “Interfaces need to be idiot-proof and require no typing, have no check boxes, and minimize the number of options for the operator,” writes the team.
  • Be ready for the worst case. In the second day of competition, the team lost the DARPA-provided communications between the operators and the robot in a full communication zone for at least six minutes, which counted against the team’s time. “Our lesson here,” they state, “is that for real robustness, we should have planned for conditions worse than expected. We could have easily programmed behaviors to be initiated autonomously if communications unexpectedly failed.”
  • The field of humanoid robots is flawed.In situations where the researchers did not control the test, most humanoid robots, even older and well-tested designs, performed poorly and often fell. “It is clear that our field needs to put much more emphasis on building reliable systems, relative to simulations and theoretical results,” writes the team. “We need to understand why our current hardware and software approaches are so unreliable.”

https://www.asme.org/engineering-topics/articles/manufacturing-design/5-lessons-darpa-robotics-challenge?utm_source=facebook&utm_medium=social&utm_content=design_manufacturing&utm_campaign=wk_101116

Atlas has gotten better.

Humanoid robots have a long way to go.  The problems in attempting to use a symbolic logic in a chaotic real world is likely to be overwhelming.  The problems is that computer power cannot keep up with the changing real world environment.  We humans have millions of years of evolution dealing with the environment that we live in.  That has been, for good or bad, been wired into our brains. So humans have a built in dataset for dealing with problems and a very fast mixed analog switching and memory system that is able to adapt incredibly fast.  No computer is likely to that for a long time, if ever.

Still, the chase is giving important knowledge on how we relate to the environment and the robots with some improvement, can go into places that people have a very high risk of death.  Of course the robot also has a high risk of death, but it’s only a robot.

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