Deep neural networks have demonstrated excellent object detection and segmentation performance from RGB data. However, these models can only recognize and predict segmentation masks with great accuracy when RGB data have sufficient information about the objects of interest. In this paper, we suggest an intelligent, active perception system that can adjust its 3D position to improve signal acquisition. The segmentation score of cluttered scene is improved a lot due to this proposed system, which can also enhance grasp pose detection for the robotic manipulator. The ResNet-50 backbone of the proposed perception system is initialized using pre-trained weights to extract a latent state from an RGB image of the cluttered scene. A Reinforcement Learning (RL) agent uses these retrieved states to reposition the visual perception system for enhancement of the underlying computer vision tasks such as segmentation of the cluttered scene. Our trained RL agent can anticipate the better position of the visual perception system, which ensures enhanced signal recovery. The effectiveness of the proposed approach is tested in a pybullet simulation environment.

Virtual memory, specifically paging, is undergoing significant innovation due to being challenged by new demands from modern workloads. Recent work has demonstrated an alternative software only design that can result in simplified hardware requirements, even supporting purely physical addressing. While we have made the case for this Compiler- And Runtime-based Address Translation (CARAT) concept, its evaluation was based on a user-level prototype. We now report on incorporating CARAT into a kernel, forming Compiler- And Runtime-based Address Translation for CollAborative Kernel Environments (CARAT CAKE). In our implementation, a Linux-compatible x64 process abstraction can be based either on CARAT CAKE, or on a sophisticated paging implementation. Implementing CARAT CAKE involves kernel changes and compiler optimizations/transformations that must work on all code in the system, including kernel code. We evaluate CARAT CAKE in comparison with paging and find that CARAT CAKE is able to achieve the functionality of paging (protection, mapping, and movement properties) with minimal overhead. In turn, CARAT CAKE allows significant new benefits for systems including energy savings, larger L1 caches, and arbitrary granularity memory management.