Hand Design

• L. Birglen, T. Laliberté, C. Gosselin. Underactuated Robotic Hands

Grasp & Manipulation

• Venkataraman, S.T., Iberall, T., Dextrous Robot Hands.
  • http://portal.acm.org/citation.cfm?id=88110&coll=GUIDE&dl=GUIDE&CFID=30490028&CFTOKEN=40686232
  • http://portal.acm.org/citation.cfm?id=88111&coll=GUIDE&dl=GUIDE&CFID=30490448&CFTOKEN=40685821
  • http://portal.acm.org/citation.cfm?id=88121&coll=GUIDE&dl=GUIDE&CFID=30490448&CFTOKEN=40685821
• R. Platt. Learning and Generalizing Control Based Grasping and Manipulation Skills. PhD thesis, University of Massachusetts Amherst, September 2006.
• A. Saxena, J. Driemeyer, J. Kearns, and A. Ng. Robotic grasping of novel objects. In Proceedings of the 2007 Neural Information Processing Systems (NIPS), 2007.
• L. K. Saul, K. Q. Weinberger, J. H. Ham, F. Sha, and D. D. Lee. Spectral methods for dimensionality reduction. In O. Chapelle, B. Schoelkopf, and A. Zien, editors, Semisupervised Learning. MIT Press, Cambridge, MA, 2006.
• A. Edsinger-Gonzales and J. Weber. Domo: A forse sensing humanoid robot for manipulation research. In Proceedings of the IEEE/RSJ International Conference on Humanoid Robotics, 2004.
• J. Coelho, J. Piater, and R. Grupen. Developing haptic and visual perceptual categories for reaching and grasping with a humanoid robot. Robotics and Autonomous Systems Journal, special issue on Humanoid Robots, 37:195–219, 2001.
• O. Khatib. Mobile manipulation: The robotic assistant. Journal of Robotics and Autonomous Systems, 26(2{3):175{183, 1999.
• Samuel Rodriguez, Jyh-Ming Lien, and N. M. Amato. Planning motion in completely deformable environments. In Proc. IEEE International Conference on Robotics and Automation, pages 2466{2471, Orlando, FL, May 2006.
• C. E. Birkhimer. Extracting Human Strategies for Use in Robotic Assembly. PhD thesis, Case Western Reserve University, Cleveland, OH, USA, January 2005.

Friction

• A. M. Al-Fahed, G. E. Stavroulakis, P. D. Panagiotopoulos. Hard and Soft Fingered Robot Grippers. The Linear Complementarity Approach
• A Generalization of the Linear Complementarity Problem

C avusoglu has been leading an eort on development of an open source open architecture framework, named GiPSi? , for developing organ-level surgical simulations [50].

Grasping of Deformable Objects. Building on the well-established form closure framework for holding rigid parts, Co-PI Goldberg introduced D-space (deformation space), a new framework for holding deformable parts [79]. This work consideres the class of deformable objects that can be modeled as linearly elastic polygons with a triangular nite element mesh, and denes the D-space of a part as the conguration space of all its mesh nodes. An object is then dened to be in deform closure when positive work is required to release the part from a grasp.

• K. \Gopal" Gopalakrishnan and Ken Goldberg. D-space and deform closure grasps of deformable parts. Int. J. Robotics Research, 24(11):899{910, November 2005.

In many cases, the control basis representation facilitates the formation of schema. For example, a general plan to first locate an object, reach to it, and then grasp it (the sequence, localize- reach-!grasp) is independent of decisions about how to locate it, and with which arm and hand to grasp. In the control basis, the general plan is captured in a sequence of potential functions, {!|%}, that convey the intention to locate, reach, and then grasp. This is a form of declarative description of the task. The procedural details—locate the object with the eyes, reach with your left arm, and grasp with your left hand—can be deferred until run-time. These details are the sensors, ", and effectors, # , that will work best given a particular context.

Motion Planning for Robotic Medical Devices. Co-PIs Alterovitz and Goldberg developed motion planning algorithms for human-controlled and robot-assisted medical needle insertion procedures, such as, biopsies and brachytherapy cancer treatment [21, 13, 8, 23, 9, 14]. A key challenge physicians face when guiding needles is tissue deformations due to needle forces. Alterovitz and Goldberg introduced planning methods that use physically-based simulation of soft tissue deformation as a component of an optimization-based planner that minimizes placement error while avoiding obstacles in the tissue. They applied this approach to both traditional clinical needles [21, 14] as well as steerable needles, a new class of exible, bevel-tip needle, co-invented by Alterovitz and Goldberg with colleagues at Johns

• Ron Alterovitz, Jean Pouliot, Richard Taschereau, I-Chow Hsu, and Ken Goldberg. Sensorless planning for medical needle insertion procedures. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), volume 3, pages 3337{3343, October 2003.

linear complementarity friction grasp - cottle

• Bgoc_Krut_Dombre_Durand_Pierrot_-_2007_-_Mechanical_design_of_a_new_pneumatically_driven_underactuated_hand.pdf
• Biagiotti_Lotti_Melchiorri_Vassura_-_2002_-_How_Far_Is_the_Human_Hand_--_A_Review_on_Anthropomorphic_Robotic_End-effectors.pdf
• Ciocarlie_Allen_-_2008_-_On-Line_Interactive_Dexterous_Grasping.pdf
• Ciocarlie_Goldfeder_Allen_-_2007_-_Dexterous_Grasping_via_Eigengrasps_A_Low-dimensional_Approach_to_a_High-complexity_Problem.pdf
• Ciocarlie_Goldfeder_Allen_-_2007_-_Dimensionality_reduction_for_hand-independent_dexterous_robotic_grasping.pdf
• Cutkosky_-_1989_-_On_Grasp_Choice_Grasp_Models_and_the_Design_of_Hands_for_Manufacturing_Tasks.pdf
• Miller_Allen_-_2004_-_GraspIt--A_Versatile_Simulator_for_Robotic_Grasping.pdf
• Petrovskaya_Park_Khatib_-_2007_-_Probabilistic_Estimation_of_Whole_Body_Contacts_for_Multi-Contact_Robot_Control.pdf
• Santello_Flanders_Soechting_-_1998_-_Postural_Hand_Synergies_for_Tool_Use.pdf

• Energid_grasp.pdf: Energid_grasp.pdf