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1. Dexterous Manipulation by Two Fingers with Coupled Joints

   Jia, Yan-Bin; Xue, Yuechuan (May 2018, IEEE International Conference on Robotics and Automation)

   This paper studies dexterous manipulation in the plane by a two-fingered hand in the plane. The dynamics of each finger, which consists of two links with coupled joints, are derived based on Lagrangian mechanics. As an object is being manipulated, its orientation and the two independent joint angles of the hand constitute the state of the entire system. Contact kinematics, accounting for both stick and slip modes, are combined with dynamics to establish a dependence of the object's linear and angular accelerations on joint accelerations. This allows control of joint torques, under a proportional-derivative (PD) law, to move the object to a target position in a desired orientation. 

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2. Dexterous manipulation by two fingers with coupled joints

   Jia, Yan-Bin; Xue, Yuechuan. (May 2018, IEEE International Conference on Robotics and Automation)

   This paper studies dexterous manipulation in the plane by a two-fingered hand in the plane. The dynamics of each finger, which consists of two links with coupled joints, are derived based on Lagrangian mechanics. As an object is being manipulated, its orientation and the two independent joint angles of the hand constitute the state of the entire system. Contact kinematics, accounting for both stick and slip modes, are combined with dynamics to establish a dependence of the object's linear and angular accelerations on joint accelerations. This allows control of joint torques, under a proportional-derivative (PD) law, to move the object to a target position in a desired orientation. 

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3. Passive Realization of Object Spatial Compliance by a Hand Having Multiple Four-Joint Hard Fingers

   https://doi.org/10.1115/1.4066168  

   Huang, Shuguang; Schimmels, Joseph M (March 2025, Journal of Mechanisms and Robotics)

   This paper presents an approach to passively realize any specified object spatial compliance using the grasp of a robotic hand. The kinematically anthropomorphic hands considered have multiple 4-joint fingers making hard point contact with the held object, and the joints of each finger have selectable passive elastic behavior. It is shown that the space of passively realizable compliances is restricted by the kinematic structure of the anthropomorphic hand. To achieve an arbitrary compliant behavior, fingers must be able to adjust their orientation. Synthesis procedures for grasps having 3, 4, and 5 or more fingers are developed. These procedures identify the finger configurations and the individual finger joint compliances needed to passively achieve any specified spatial object compliance matrix in the 20-dimensional subspace of grasp-realizable behaviors. 

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4. Grasping Objects Big and Small: Human Heuristics Relating Grasp-Type and Object Size

   https://doi.org/10.1109/icra.2018.8460860  

   Kothari, Ammar; Morrow, John; Thrasher, Victoria; Engle, Kadon; Balasubramanian, Ravi; Grimm, Cindy (May 2018, IEEE International Conference on Robotics and Automation)

   This paper presents an online data collection method that captures human intuition about what grasp types are preferred for different fundamental object shapes and sizes. Survey questions are based on an adopted taxonomy that combines grasp pre-shape, approach, wrist orientation, object shape, orientation and size which covers a large swathe of common grasps. For example, the survey identifies at what object height or width dimension (normalized by robot hand size) the human prefers to use a two finger precision grasp versus a three-finger power grasp. This information is represented as a confidence-interval based polytope in the object shape space. The result is a database that can be used to quickly find potential pre-grasps that are likely to work, given an estimate of the object shape and size. 

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5. Using Geometric Features to Represent Near-Contact Behavior in Robotic Grasping

   https://doi.org/10.1109/ICRA.2019.8793779  

   Dessalene, Eadom; Ong, Yi Herng; Morrow, John; Balasubramanian, Ravi; Grimm, Cindy (May 2019, International Conference on Robotics and Applications)

   In this paper we define two feature representations for grasping. These representations capture hand-object geometric relationships at the near-contact stage - before the fingers close around the object. Their benefits are: 1) They are stable under noise in both joint and pose variation. 2) They are largely hand and object agnostic, enabling direct comparison across different hand morphologies. 3) Their format makes them suitable for direct application of machine learning techniques developed for images. We validate the representations by: 1) Demonstrating that they can accurately predict the distribution of ε-metric values generated by kinematic noise. I.e., they capture much of the information inherent in contact points and force vectors without the corresponding instabilities. 2) Training a binary grasp success classifier on a real-world data set consisting of 588 grasps. 

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