GPA-RAM: Grasp-Pretraining Augmented Robotic Attention Mamba for Spatial Task Learning

Most existing robot manipulation methods prioritize task learning by enhancing perception through complex deep network architectures. However, they face challenges in real-time collision-free planning. Hence, Robotic Attention Mamba (RAM) is designed for refined planning. Specifically, by integrating Mamba and parallel single-view attention, RAM aligns multi-view vision and task-related language features, ensuring efficient fine-grained task planning with linear complexity and robust real-time performance. Nevertheless, it has the potential for further improvement in high-precision grasping and manipulation. Thus, Grasp-Pretraining Augmentation (GPA) is devised, with a grasp pose feature extractor pretrained utilizing object grasp poses directly inherited from whole-task demonstrations. Subsequently, the extracted grasp features are fused with the spatially aligned planning features from RAM through attention-based Pre-trained Location Fusion, preserving high-resolution grasping cues overshadowed by an overemphasis on global planning. To summarize, we propose Grasp-Pretraining Augmented Robotic Attention Mamba (GPA-RAM), dividing spatial task learning into RAM for planning skill learning and GPA for grasping skill learning. GPA-RAM demonstrates superior performance across three robot systems with distinct camera configurations in simulation and the real world. Compared with previous state-of-the-art methods, it improves the absolute success rate by 8.2% (from 79.3% to 87.5%) on the RLBench multi-task benchmark and 40\% (from 16% to 56%), 12% (from 86% to 98%) on the ALOHA bimanual manipulation tasks, while delivering notably faster inference. Furthermore, experimental results demonstrate that both RAM and GPA enhance task learning, with GPA proving robust to different architectures of pretrained grasp pose feature extractors. The website is: https://logssim.github.io/GPA\_RAM\_website/.

CTR is not Enough: a Novel Reinforcement Learning based Ranking Approach for Optimizing Session Clicks

Ranking is a crucial module using in the recommender system. In particular, the ranking module using in our YoungTao recommendation scenario is to provide an ordered list of items to users, to maximize the click number throughout the recommendation session for each user. However, we found that the traditional ranking method for optimizing Click-Through rate(CTR) cannot address our ranking scenario well, since it completely ignores user leaving, and CTR is the optimization goal for the one-step recommendation. To effectively undertake the purpose of our ranking module, we propose a long-term optimization goal, named as CTE (Click-Through quantity expectation), for explicitly taking the behavior of user leaving into account. Based on CTE, we propose an effective model trained by reinforcement learning. Moreover, we build a simulation environment from offline log data for estimating PBR and CTR. We conduct extensive experiments on offline datasets and an online e-commerce scenario in TaoBao. Experimental results show that our method can boost performance effectively