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Creators/Authors contains: "Calli, Berk"

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  1. This paper presents a visual servoing method for controlling a robot in the configuration space by purely using its natural features. We first created a data collection pipeline that uses camera intrinsics, extrinsics, and forward kinematics to generate 2D projections of a robot's joint locations (keypoints) in image space. Using this pipeline, we are able to collect large sets of real-robot data, which we use to train realtime keypoint detectors. The inferred keypoints from the trained model are used as control features in an adaptive visual servoing scheme that estimates, in runtime, the Jacobian relating the changes of the keypoints and joint velocities. We compared the 2D configuration control performance of this method to the skeleton-based visual servoing method (the only other algorithm for purely vision-based configuration space visual servoing), and demonstrated that the keypoints provide more robust and less noisy features, which result in better transient response. We also demonstrate the first vision-based 3D configuration space control results in the literature, and discuss its limitations. Our data collection pipeline is available at which can be utilized to collect image datasets and train realtime keypoint detectors for various robots and environments. 
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  2. Free, publicly-accessible full text available May 29, 2024
  3. This paper presents a novel visual servoing method that controls a robotic manipulator in the configuration space as opposed to the classical vision-based control methods solely focusing on the end effector pose. We first extract the robot's shape from depth images using a skeletonization algorithm and represent it using parametric curves. We then adopt an adaptive visual servoing scheme that estimates the Jacobian online relating the changes of the curve parameters and the joint velocities. The proposed scheme does not only enable controlling a manipulator in the configuration space, but also demonstrates a better transient response while converging to the goal configuration compared to the classical adaptive visual servoing methods. We present simulations and real robot experiments that demonstrate the capabilities of the proposed method and analyze its performance, robustness, and repeatability compared to the classical algorithms. 
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    In this work, we present several heuristic-based and data-driven active vision strategies for viewpoint optimization of an arm-mounted depth camera to aid robotic grasping. These strategies aim to efficiently collect data to boost the performance of an underlying grasp synthesis algorithm. We created an open-source benchmarking platform in simulation ( ), and provide an extensive study for assessing the performance of the proposed methods as well as comparing them against various baseline strategies. We also provide an experimental study with a real-world two finger parallel jaw gripper setup by utilizing an existing grasp planning benchmark in the literature. With these analyses, we were able to quantitatively demonstrate the versatility of heuristic methods that prioritize certain types of exploration, and qualitatively show their robustness to both novel objects and the transition from simulation to the real world. We identified scenarios in which our methods did not perform well and objectively difficult scenarios, and present a discussion on which avenues for future research show promise. 
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