EarlyCommit：面向机器人的逐词校准承诺，实现指令未完成时提前行动
EarlyCommit: Calibrated Word-by-Word Commitment for Robots Acting Before You Finish Speaking
AI总结：
本文提出开源工具EarlyCommit，让机器人在指令未完成时逐词校准承诺，实验显示其在未见过的词指令上表现优于关键词匹配，正确率高且处理速度快。
摘要：
人类听者往往能在指令结束前几个词就识别出意图对象，但机器人通常被设计为等待说话人完成指令后再决定行动。我们提出EarlyCommit，这是一款开源工具，可让机器人在指令被说出过程中就对意图对象做出承诺。每识别一个词后，微调后的文本编码器会对场景中的每个对象进行评分；当最佳对象的校准概率达到用户设置的阈值τ时，机器人就会行动，其中1-τ是接受选错对象的风险，否则机器人会等待或询问。该工具包含流式语音前端、带有逐词精确真值的场景与指令生成器，以及训练、校准和评估脚本。在包含训练时未见过的词的指令上，EarlyCommit的正确承诺率是关键词匹配的1.6至2倍；当τ=0.9时，其承诺时的正确率至少为92%；在消费级GPU上，每个词的处理时间为31至35毫秒。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
