AI 中文总结
CLAP通过压力模块使吸附状态可观测，融合视觉与本体感觉，终止开环执行，在真实机器人上实现96.67%平均成功率，显著优于基线。
AI 中文摘要
堆叠和码垛要求精确放置：一层中遗留的误差会被下一层继承，而平面吸盘没有特征可将错误姿态引导至正确姿态。自上而下的吸取适合这种密集排列，且吸取已被引入视觉-语言-动作（VLA）策略。然而，该工作未报告的是，策略是否基于测量的真空信号进行条件化，或是否利用该信号放弃已在进行中的动作。在此，视觉无法解决该问题，因为在关键时刻，吸杯与其吸附的面相互遮挡。我们提出CLAP，通过接入真空管路的压力模块使吸附状态可观测。解码读数取代策略本体感觉中的吸取命令，与视觉特征融合，并终止开环执行窗口，使策略根据新观测重新推断。对于数据，我们在物理机器人上记录目标状态拆卸，并离线反转关节状态序列，无需模拟重放。定向阶段演示，占训练帧的8.3%，覆盖吸取转换及中断抓取留下的配置。在真实的Unitree Z1上，一个多任务检查点在两种颜色设置下平均成功率达96.67%，分别高出最强基线16.67和10.00个百分点，其单色四块成功平均误差为15.92毫米。四个消融设置落后5.00至13.33个百分点。我们将发布代码和训练权重。
英文摘要
Stacking and palletising demand precise placement: error left in one layer is inherited by the next, and a flat pad offers no feature to funnel a wrong pose into the right one. Top-down suction suits such dense arrangements, and suction has already been brought into vision-language-action (VLA) policies. What that work does not report, however, is a policy conditioned on a measured vacuum signal, or one that uses it to abandon an action already under way. Vision does not settle the question here, because at the moment it matters the cup and the face it holds occlude each other. We present CLAP, which makes the attachment state observable through a pressure module tapped into the vacuum line. The decoded reading replaces the suction command in the policy's proprioception, is fused with the visual features, and terminates the open-loop execution window so that the policy re-infers from a fresh observation. For data, we record goal-state disassembly on the physical robot and reverse the joint-state sequence offline, without a simulation replay. Targeted phase demonstrations, 8.3% of the training frames, cover the suction transitions and the configurations an interrupted grasp leaves behind. On a real Unitree Z1, one multi-task checkpoint reaches 96.67% average success in both colour settings, 16.67 and 10.00 points above the strongest baseline, its monochromatic four-block successes averaging 15.92 mm of error. Four ablation settings fall 5.00 to 13.33 points short. We will release code and trained weights.
Comments8 pages, 6 figures
