# Robot motion planning

Researchers at MIT and New York University introduce FLEX, a flexible feature-editing pipeline for robot motion planning that lets a robotic arm go beyond the literal wording of a natural-language instruction and infer a higher-level intent the person never stated. Earlier methods added one object to an avoidance list per correction, so the list grew long and went stale as the environment changed. FLEX uses a large language model to generalize across the features already named and a vision system to drop objects no longer present; in three simulated tasks, serving coffee, moving a candle and carrying a bowling ball, the robot followed the literal command and extended avoidance to electronics, flammable objects and fragile items.

Researchers at MIT and New York University introduce FLEX, a flexible feature-editing pipeline for robot motion planning that lets a robotic arm go beyond the literal wording of a natural-language instruction and infer a higher-level intent the person never stated. Earlier methods added one object to an avoidance list per correction, so the list grew long and went stale as the environment changed. FLEX uses a large language model to generalize across the features already named and a vision system to drop objects no longer present; in three simulated tasks, serving coffee, moving a candle and carrying a bowling ball, the robot followed the literal command and extended avoidance to electronics, flammable objects and fragile items.