A research team in the Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory developed an algorithm that redefines robotic motion safety by allowing for "safe impacts" on top of collision avoidance.
Credit: MIT CSAIL
Researchers at the Massachusetts Institute of Technology have designed an algorithm to help a robot efficiently dress a human, theoretically ensuring human safety by reasoning about the human model's uncertainty.
The team declined to use a single default model in which the machine only understands one potential reaction in favor of many possible models, to more closely emulate how a human understands other humans.
The robot reduces uncertainty and refines those models by collecting more data.
The MIT team also reclassified safety for human-aware motion planners as either collision avoidance or safe impact in case of a collision, so the robot could safely complete the dressing task faster.
Carnegie Mellon University's Zackory Erickson said, "This research could potentially be applied to a wide variety of assistive robotics scenarios, towards the ultimate goal of enabling robots to provide safer physical assistance to people with disabilities."
From Engadget
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