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."
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