去掉负样本学习信号，机器人拉链任务成功率从 86% 跌至 60%

micoolcho · x · 2026-11-08

@fancyyzc's team proposes TRFO, showing that learning from negative experience measurably matters for robot policy training.

Excluding negative-advantage samples cuts zip-tie success from 86% to 60%;
Removing the trust-region constraint causes training to diverge;
With the same collected experience, TRFO outperforms HG-DAgger and RECAP on laundry folding and zip-tie threading.

Conclusion: both the negative learning signal and constraining its strength contribute to effective policy improvement.

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