Nonetheless, to fill the gap between robot and human perception, multisensory approaches have recently been studied and evolved in novel robotic platforms, combining visual data with vocal interfaces, or tactile sensors. Intelligent DL-based behaviors are also desired for visual-based robotic surgery to detect and segment instruments, and in many industrial robotic tasks. For instance, relevant works in the precision agriculture field have been proposed in recent years to support autonomous navigation, harvesting, and spraying. DL has been used in many robotics applications where objects must be detected or segmented to address manipulation tasks, demonstrating competitive advantages compared to classic image processing algorithms in terms of accuracy and robustness. Nowadays, advances in computer vision for robotic perception are strictly tied to deep learning (DL).

In recent years, visual-based control strategies have successfully spread in a wide variety of robotics contexts. Our experimentation shows that our low-cost solution allows e.DO to precisely reach removable blocks and perform up to 14 consecutive extractions in a row. We integrated the visual-based strategy with a 1D force sensor to detect whether the block could be safely removed by identifying a force threshold value. To this aim, we trained an instance segmentation deep learning model on a synthetic custom dataset to segment each piece of the Jenga tower, allowing for visual tracking of the desired block’s pose during the motion of the manipulator. Our solution focuses on a visual-based control strategy to accurately align the end-effector with the desired block, enabling block extraction by pushing. In this work, we propose a novel, cost-effective architecture for playing Jenga with e.Do, a 6DOF anthropomorphic manipulator manufactured by Comau, a standard depth camera, and an inexpensive monodirectional force sensor. A Jenga game involves many traits of complex industrial and surgical manipulation tasks, requiring a multi-step strategy, the combination of visual and tactile data, and the highly precise motion of a robotic arm to perform a single block extraction. Indeed, it encourages the study of novel robotics methods to successfully extract blocks from a tower. One hacky way to get around this is to set NetworkOwnership of all the blocks (and the prop hitting them) to the server, which in my tests does appear to make everything way smoother, however its possible that this will lower performance (especially when you have alot of blocks like in my case, but for you it might just be alright).The game of Jenga is a benchmark used for developing innovative manipulation solutions for complex tasks. Another issue you might have is that roblox physics isnt the best and since unanchored parts are owned by closest players sometimes hitting the blocks wont move them cause the client who owns them didnt register the hit. I got around this by simply increasing the density of the thrown object as much as possible, then lowering the density and friction of the blocks being hit (it also helps that for me blocks being hit are made out of wood). You are hitting a metal block with a plastic object (meaning the mass of the object being hit is greater than the mass of the object hitting it, so it wont move much). Im working on a similar game and what others said is right.

4 push ups or shot jenga