EarlyCommit: Calibrated Word-by-Word Commitment for Robots Acting Before You Finish Speaking

Abstract

Human listeners can often identify the intended object several words before an instruction ends. Robots, however, are typically designed to wait until the speaker has finished before deciding how to act. We present EarlyCommit, an open-source tool that lets a robot commit to the intended object while the instruction is being spoken. After every recognized word, a fine-tuned text encoder scores each object in the scene. The robot acts when the calibrated probability of the best object reaches a threshold tau set by the user, so 1 - tau is the accepted risk of choosing the wrong object. Otherwise, it waits or asks. The tool includes a streaming speech front end, a scene and instruction generator with exact word-by-word ground truth, and scripts to train, calibrate, and evaluate. On instructions containing words never seen in training, EarlyCommit commits correctly 1.6 to 2 times as often as keyword matching, is right at least 92 percent of the time when it commits at tau = 0.9, and needs 31 to 35 ms per word on consumer GPUs.