NowWAM: Current-Frame Denoising Outperforms Future Prediction in Robot Control
机器人学会“看现在”，比学会“想未来”更重要
A 2026 paper introduces NowWAM, a robot control method that trains on current-frame denoising trajectories instead of predicting future frames. Experiments show this approach matches or exceeds the robustness of future-prediction baselines like ImageWAM while halving visual token usage and accelerating training speed by 1.8x. The method works with standard text-to-image diffusion models, eliminating the need for video-generation capabilities.
Source: Chinese robotics — Research news discovery · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported success rate
82.89%
Reported trials
View original evidence
他们固定其他所有条件不变，只改训练时用的辅助目标是未来帧还是过去帧，结果发现：用未来16步和32步的画面当目标，成功率分别是82.89%和83.70%；换成用过去16步的画面当目标，成功率反而还高一点，83.80%。
Open source S13Reported success rate
83.7%
Reported trials
View original evidence
他们固定其他所有条件不变，只改训练时用的辅助目标是未来帧还是过去帧，结果发现：用未来16步和32步的画面当目标，成功率分别是82.89%和83.70%；换成用过去16步的画面当目标，成功率反而还高一点，83.80%。
Open source S13Reported success rate
79.1%
Reported trials
View original evidence
论文的消融实验给出了答案：保留两条视觉流（当前目标单独作为辅助目标）成功率是79.10%，比起当前帧去噪这种单流方案，明显要低一截，而且还得额外处理一倍的视觉信息量。
Open source S19Reported success rate
77.48%
Reported trials
View original evidence
消融实验显示，如果训练时只固定用"完全干净"的画面（也就是不引入任何噪声变化），成功率只有77.48%；而让噪声强度均匀分布，成功率能到83.46%；再把采样往低噪声方向偏移一点，成功率进一步提到84.97%。
Open source S21Reported success rate
83.46%
Reported trials
View original evidence
消融实验显示，如果训练时只固定用"完全干净"的画面（也就是不引入任何噪声变化），成功率只有77.48%；而让噪声强度均匀分布，成功率能到83.46%；再把采样往低噪声方向偏移一点，成功率进一步提到84.97%。
Open source S21Reported success rate
84.97%
Reported trials
View original evidence
消融实验显示，如果训练时只固定用"完全干净"的画面（也就是不引入任何噪声变化），成功率只有77.48%；而让噪声强度均匀分布，成功率能到83.46%；再把采样往低噪声方向偏移一点，成功率进一步提到84.97%。
Open source S21success rate difference
>7
- paper: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
他们固定其他所有条件不变，只改训练时用的辅助目标是未来帧还是过去帧，结果发现：用未来16步和32步的画面当目标，成功率分别是82.89%和83.70%；换成用过去16步的画面当目标，成功率反而还高一点，83.80%。
Open source S13
论文的消融实验给出了答案：保留两条视觉流（当前目标单独作为辅助目标）成功率是79.10%，比起当前帧去噪这种单流方案，明显要低一截，而且还得额外处理一倍的视觉信息量。
Open source S19
消融实验显示，如果训练时只固定用"完全干净"的画面（也就是不引入任何噪声变化），成功率只有77.48%；而让噪声强度均匀分布，成功率能到83.46%；再把采样往低噪声方向偏移一点，成功率进一步提到84.97%。
Open source S21
如果不这样做，而是只在最理想状态下训练，论文的数据已经摆在那了：77.48%对84.97%，差了超过7个百分点。
Open source S23
Source：Chinese robotics — Research news discovery · msn.cn