Accurate localization of graspable regions within a single object point cloud is critical to enable task-based robot grasps. State-of-the-art task-based robot grasp synthesis methods fit over-approximated 3D bounding boxes that, in some cases, fail to isolate graspable regions even if they exist. While deep learning or geometrical shape decomposition methods can offer improved approximations, they lack guarantees for the graspability of segmented regions, require prior knowledge of the object, and/or demand large annotated datasets for fine-tuning. In this paper, we overcome these limitations to introduce ITSI (Iterative Slicing). ITSI is a complete, taskoriented grasp synthesis approach that functions independently of object-specific knowledge. ITSI effectively segments multiple graspable regions that conform to the constraints of robot grippers, thereby enabling compatibility with any object a robot seeks to grasp and any robot gripper size. Our extensive realworld and simulation experiments on diverse object datasets demonstrate how ITSI dramatically increases the number of discoverable robot grasps by up to 44 % when compared to the state-of-the-art. We also expand ITSI's capabilities beyond task-based robot grasp synthesis to highlight its performance in human affordance segmentation, where our performance is comparable to fully supervised deep-learning based methods (in fact, we outperform them by 1 %).

Containerized vertical farming is a type of vertical farming practice using hydroponics in which plants are grown in vertical layers within a mobile shipping container. Space limitations within shipping containers make the automation of different farming operations challenging. In this paper, we explore the use of cobots (i.e., collaborative robots) to automate two key farming operations, namely, the transplantation of saplings and the harvesting of grown plants. Our method uses a single demonstration from a farmer to extract the motion constraints associated with the tasks, namely, transplanting and harvesting, and can then generalize to different instances of the same task. For transplantation, the motion constraint arises during insertion of the sapling within the growing tube, whereas for harvesting, it arises during extraction from the growing tube. We present experimental results to show that using RGBD camera images (obtained from an eye-in-hand configuration) and one demonstration for each task, it is feasible to perform transplantation of saplings and harvesting of leafy greens using a cobot, without task-specific programming.

In this paper, we study the problem of task-oriented grasp synthesis from partial point cloud data using an eye-in-hand camera configuration. In task-oriented grasp synthesis, a grasp has to be selected so that the object is not lost during manipulation, and it is also ensured that adequate force/moment can be applied to perform the task. We formalize the notion of a gross manipulation task as a constant screw motion (or a sequence of constant screw motions) to be applied to the object after grasping. Using this notion of task, and a corresponding grasp quality metric developed in our prior work, we use a neural network to approximate a function for predicting the grasp quality metric on a cuboid shape. We show that by using a bounding box obtained from the partial point cloud of an object, and the grasp quality metric mentioned above, we can generate a good grasping region on the bounding box that can be used to compute an antipodal grasp on the actual object. Our algorithm does not use any manually labeled data or grasping simulator, thus making it very efficient to implement and integrate with screw linear interpolation-based motion planners. We present simulation as well as experimental results that show the effectiveness of our approach. Website: https://irsl-sbu.github.io/Task-Oriented-Grasping-from-Point-Cloud-Representation/.

Text correction on mobile devices usually requires precise and repetitive manual control. In this paper, we present EyeSayCorrect, an eye gaze and voice based hands-free text correction method for mobile devices. To correct text with EyeSayCorrect, the user first utilizes the gaze location on the screen to select a word, then speaks the new phrase. EyeSayCorrect would then infer the user’s correction intention based on the inputs and the text context. We used a Bayesian approach for determining the selected word given an eye-gaze trajectory. Given each sampling point in an eye-gaze trajectory, the posterior probability of selecting a word is calculated and accumulated. The target word would be selected when its accumulated interest is larger than a threshold. The misspelt words have higher priors. Our user studies showed that using priors for misspelt words reduced the task completion time up to 23.79% and the text selection time up to 40.35%, and EyeSayCorrect is a feasible hands-free text correction method on mobile devices.