00743nas a2200265 4500008003900000245004900039210004800088300001400136100001500150700001800165700001400183700001600197700001700213700001600230700001400246700001400260700001500274700001400289700002000303700001600323700001700339700001500356700001500371856009100386 2018 d00aAdvanced grasping with the Pisa/IIT softHand0 aAdvanced grasping with the PisaIIT softHand app. 19-381 aBonilla, M1 aDella Santina1 aRocchi, A1 aLuberto, E.1 aSantaera, G.1 aFarnioli, E1 aPiazza, C1 aBonomo, F1 aBrando, A.1 aRaugi, A.1 aCatalano, M. G.1 aBianchi, M.1 aGarabini, M.1 aGrioli, G.1 aBicchi, A. uhttp://www.centropiaggio.unipi.it/publications/advanced-grasping-pisaiit-softhand.html01821nas a2200241 4500008004100000245015800041210006900199300001600268490000600284520098400290100001801274700001501292700001701307700001501324700001501339700001401354700002001368700001501388700001601403700001501419700001701434856012801451 2018 eng d00aEfficient Walking Gait Generation via Principal Component Representation of Optimal Trajectories: Application to a Planar Biped Robot With Elastic Joints0 aEfficient Walking Gait Generation via Principal Component Repres a2299–23060 v33 a
Recently, the method of choice to exploit robot dynamics for efficient walking is numerical optimization (NO). The main drawback in NO is the computational complexity, which strongly affects the time demand of the solution. Several strategies can be used to make the optimization more treatable and to efficiently describe the solution set. In this letter, we present an algorithm to encode effective walking references, generated offline via numerical optimization, extracting a limited number of principal components and using them as a basis of optimal motions. By combining these components, a good approximation of the optimal gaits can be generated at run time. The advantages of the presented approach are discussed, and an extensive experimental validation is carried out on a planar legged robot with elastic joints. The biped thus controlled is able to start and stop walking on a treadmill, and to control its speed dynamically as the treadmill speed changes.
1 aGasparri, G M1 aManara, S.1 aCaporale, D.1 aAverta, G.1 aBonilla, M1 aMarino, H1 aCatalano, M. G.1 aGrioli, G.1 aBianchi, M.1 aBicchi, A.1 aGarabini, M. uhttp://www.centropiaggio.unipi.it/publications/efficient-walking-gait-generation-principal-component-representation-optimal01717nas a2200529 4500008004100000245009400041210006900135260000900204300001400213490000600227653003200233653001700265653002600282653003000308653002400338653003500362653001000397653002300407653001900430653001300449653001300462653003200475653003000507653003500537653002200572653003700594653003900631653001700670653002400687653002000711653002800731653002300759653002400782653003300806653003800839653003200877653001100909653001900920653002000939653001800959100001500977700001700992700001501009700001601024700001501040856013201055 2018 eng d00aIncrementality and Hierarchies in the Enrollment of Multiple Synergies for Grasp Planning0 aIncrementality and Hierarchies in the Enrollment of Multiple Syn cJuly a2686-26930 v310a19-DoF anthropomorphic hand10aBiomechanics10acommon grasping tasks10acompliant joint/mechanism10acovariance matrices10aexperimental covariance matrix10aForce10aforce distribution10agrasp planning10aGrasping10agrippers10ahand posture reconstruction10ahand-object relative pose10aincremental learning algorithm10aJacobian matrices10ajoint angle covariation patterns10alearning (artificial intelligence)10aminimisation10amultifingered hands10apose estimation10apostural hand synergies10apostural synergies10aposture description10aprincipal component analysis10areduced complexity representation10arelative statistical weight10aRobots10aSolid modeling10asynergy vectors10aTask analysis1 aAverta, G.1 aAngelini, F.1 aBonilla, M1 aBianchi, M.1 aBicchi, A. uhttp://www.centropiaggio.unipi.it/publications/incrementality-and-hierarchies-enrollment-multiple-synergies-grasp-planning.html00732nas a2200205 4500008003900000245010600039210006900145100001600214700001500230700001800245700001500263700001500278700001400293700001700307700001700324700001500341700002000356700001500376856013500391 2018 d00aTouch-Based Grasp Primitives for Soft Hands: Applications to Human-to-Robot Handover Tasks and Beyond0 aTouchBased Grasp Primitives for Soft Hands Applications to Human1 aBianchi, M.1 aAverta, G.1 aBattaglia, E.1 aRosales, C1 aBonilla, M1 aTondo, A.1 aPoggiani, M.1 aSantaera, G.1 aCiotti, S.1 aCatalano, M. G.1 aBicchi, A. uhttp://www.centropiaggio.unipi.it/publications/touch-based-grasp-primitives-soft-hands-applications-human-robot-handover-tasks-and02171nas a2200157 4500008003900000245008200039210006900121260000900190300001600199520168700215653001301902100001501915700001901930700001501949856004901964 2017 d00aNoninteracting Constrained Motion Planning and Control for Robot Manipulators0 aNoninteracting Constrained Motion Planning and Control for Robot bIEEE a4038 - 40433 aIn this paper we present a novel geometric approach
to motion planning for constrained robot systems.
This problem is notoriously hard, as classical sampling-based
methods do not easily apply when motion is constrained in
a zero-measure submanifold of the configuration space. Based
on results on the functional controllability theory of dynamical
systems, we obtain a description of the complementary spaces
where rigid body motions can occur, and where interaction
forces can be generated, respectively. Once this geometric setting
is established, the motion planning problem can be greatly
simplified. Indeed, we can relax the geometric constraint, i.e.,
replace the lower–dimensional constraint manifold with a fulldimensional
boundary layer. This in turn allows us to plan
motion using state-of-the-art methods, such as RRT*, on points
within the boundary layer, which can be efficiently sampled. On
the other hand, the same geometric approach enables the design
of a completely decoupled control scheme for interaction forces,
so that they can be regulated to zero (or any other desired
value) without interacting with the motion plan execution.
A distinguishing feature of our method is that it does not
use projection of sampled points on the constraint manifold,
thus largely saving in computational time, and guaranteeing
accurate execution of the motion plan. An explanatory example
is presented, along with an experimental implementation of the
method on a bimanual manipulation workstation.
Taking inspiration from the neuroscientific findings on hand synergies discussed in the first part of the book, in this chapter we present the Pisa/IIT SoftHand, a novel robot hand prototype. The design moves under the guidelines of making an hardware robust and easy to control, preserving an high level of grasping capabilities and an aspect as similar as possible to the human counterpart. First, the main theoretical tools used to enable such simplification are presented, as for example the notion of soft synergies. A discussion of some possible actuation schemes shows that a straightforward implementation of the soft synergy idea in an effective design is not trivial. The proposed approach, called adaptive synergy, rests on ideas coming from underactuated hand design, offering a design method to implement the desired set of soft synergies as demonstrated both with simulations and experiments. As a particular instance of application of the synthesis method of adaptive synergies, the Pisa/IIT SoftHand is described in detail. The hand has 19 joints, but only uses one actuator to activate its adaptive synergy. Of particular relevance in its design is the very soft and safe, yet powerful and extremely robust structure, obtained through the use of innovative articulations and ligaments replacing conventional joint design. Moreover, in this work, summarizing results presented in previous papers, a discussion is presented about how a new set of possibilities is open from paradigm shift in manipulation approaches, moving from manipulation with rigid to soft hands.
1 aCatalano, M. G.1 aGrioli, G.1 aFarnioli, E1 aSerio, A.1 aBonilla, M1 aGarabini, M.1 aPiazza, C1 aGabiccini, M1 aBicchi, A. uhttp://www.centropiaggio.unipi.it/publications/soft-adaptive-synergies-pisaiit-softhand.html00983nas a2200361 4500008004100000245008000041210006900121260001200190490000700202653001300209100001600222700002000238700002000258700001400278700001200292700001500304700002200319700001700341700001900358700002100377700001500398700002100413700001400434700001500448700001500463700001400478700001500492700001500507700001300522700001800535700001900553856004900572 2016 eng d00aNo More Heavy Lifting: Robotic Solutions to the Container Unloading Problem0 aNo More Heavy Lifting Robotic Solutions to the Container Unloadi c08/20160 v2310aRobotics1 aStoyanov, T1 aVaskeviciusz, N1 aMueller, C., A.1 aFromm, T.1 aKrug, R1 aTincani, V1 aMojtahedzadeh, R.1 aKunaschk, S.1 aErnits, R., M.1 aCanelhas, D., R.1 aBonilla, M1 aSchwertfeger, S.1 aBonini, M1 aHalfar, H.1 aPathak, K.1 aRohde, M.1 aFantoni, G1 aBicchi, A.1 aBirk, A.1 aLilienthal, A1 aEchelmeyer, W. uhttps://ieeexplore.ieee.org/document/755353100700nas a2200169 4500008003900000245007500039210006900114260006000183300001400243653001200257653001300269100001500282700001600297700001700313700001500330856018500345 2015 d00aGrasp Planning with Soft Hands using Bounding Box Object Decomposition0 aGrasp Planning with Soft Hands using Bounding Box Object Decompo aHamburg, Germany, September 28 - October 02, 2015bIEEE a518 - 52310aHaptics10aRobotics1 aBonilla, M1 aResasco, D.1 aGabiccini, M1 aBicchi, A. uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7353421&queryText=Grasp%20Planning%20with%20Soft%20Hands%20using%20Bounding%20Box%20Object%20Decomposition&newsearch=true00609nas a2200157 4500008003900000245008500039210006900124260003600193300001600229653001300245100001500258700001600273700001900289700001500308856012800323 2015 d00aSample-Based Motion Planning for Robot Manipulators with Closed Kinematic Chains0 aSampleBased Motion Planning for Robot Manipulators with Closed K aSeattle, USA, 25 - 30 MaybIEEE a2522 - 252710aRobotics1 aBonilla, M1 aFarnioli, E1 aPallottino, L.1 aBicchi, A. uhttp://www.centropiaggio.unipi.it/publications/sample-based-motion-planning-robot-manipulators-closed-kinematic-chains.html02519nas a2200229 4500008003900000245002900039210002900068260003600097300001400133520187600147653001202023653001302035100001502048700001602063700001402079700002002093700001502113700001702128700001702145700001502162856011202177 2014 d00aGrasping with Soft Hands0 aGrasping with Soft Hands aMadrid, Spain, November 18 - 20 a581 - 5873 aDespite some prematurely optimistic claims, the ability of robots to grasp general objects in unstructured environments still remains far behind that of humans. This is not solely caused by differences in the mechanics of hands: indeed, we show that human use of a simple robot hand (the Pisa/IIT SoftHand) can afford capabilities that are comparable to natural grasping. It is through the observation of such human-directed robot hand operations that we realized how fundamental in everyday grasping and manipulation is the role of hand compliance, which is used to adapt to the shape of surrounding objects. Objects and environmental constraints are in turn used to functionally shape the hand, going beyond its nominal kinematic limits by exploiting structural softness. In this paper, we set out to study grasp planning for hands that are simple — in the sense of low number of actuated degrees of freedom (one for the Pisa/IIT SoftHand) — but are soft, i.e. continuously deformable in an infinity of possible shapes through interaction with objects. After general considerations on the change of paradigm in grasp planning that this setting brings about with respect to classical rigid multi-dof grasp planning, we present a procedure to extract grasp affordances for the Pisa/IIT SoftHand through physically accurate numerical simulations. The selected grasps are then successfully tested in an experimental scenario.
10aHaptics10aRobotics1 aBonilla, M1 aFarnioli, E1 aPiazza, C1 aCatalano, M. G.1 aGrioli, G.1 aGarabini, M.1 aGabiccini, M1 aBicchi, A. uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7041421&queryText%3DGrasping+with+Soft+Hands01718nas a2200241 4500008003900000245009900039210006900138260004500207300001600252520097600268653001201244653001301256100001201269700001601281700001501297700001501312700002001327700001501347700001301362700001801375700001501393856006801408 2014 d00aVelvet Fingers: Grasp Planning and Execution for an Underactuated Gripper with Active Surfaces0 aVelvet Fingers Grasp Planning and Execution for an Underactuated aHong Kong, May 31 2014-June 7 2014bIEEE a3669 - 36753 aIn this work we tackle the problem of planning grasps for an underactuated gripper which enable it to retrieve target objects from a cluttered environment. Furthermore, we investigate how additional manipulation capabilities of the gripping device, provided by active surfaces on the inside of the fingers, can lead to performance improvement in the grasp execution process. To this end, we employ a simple strategy, in which the target object is `pulled-in' towards the palm during grasping which results in firm enveloping grasps. We show the effectiveness of the suggested methods by means of experiments conducted in a real-world scenario.
10aHaptics10aRobotics1 aKrug, R1 aStoyanov, T1 aBonilla, M1 aTincani, V1 aVaskeviciusz, N1 aFantoni, G1 aBirkz, A1 aLilienthal, A1 aBicchi, A. uhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=690739001630nas a2200205 4500008004100000245008400041210006900125260003100194300001600225520091300241653001301154100001501167700001501182700002001197700001501217700001701232700001501249700001501264856014501279 2013 eng d00aControlling the active surfaces of the Velvet Fingers: sticky to slippy fingers0 aControlling the active surfaces of the Velvet Fingers sticky to aTokyo, JapancNovember 3-7 a5494 - 54993 aIndustrial grippers are often used for grasping, while in-hand re-orientation and positioning are dealt with by other means. Contact surface engineering has been recently proposed as a possible mean to introduce dexterity in simple grippers, as in the Velvet Fingers smart gripper, a novel concept of end-effector combining simple under-actuated mechanics and high manipulation possibilities, thanks to conveyors which are built in the finger pads. This paper undergoes the modeling and control of the active conveyors of the Velvet Fingers gripper which are rendered able to emulate different levels of friction and to apply tangential thrusts to the contacted objects. Through the paper particular attention is dedicated to the mechanical implementation, sense drive and control electronics of the device. The capabilities of the prototype are showed in some grasping and manipulation experiments.
10aRobotics1 aTincani, V1 aGrioli, G.1 aCatalano, M. G.1 aBonilla, M1 aGarabini, M.1 aFantoni, G1 aBicchi, A. uhttp://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6697152&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D669715200604nas a2200157 4500008004100000245008500041210006900126260003100195300001500226653001300241100001600254700001700270700001500287700001500302856012900317 2013 eng d00aGrasp Compliance Regulation in Synergistically Controlled Robotic Hands with VSA0 aGrasp Compliance Regulation in Synergistically Controlled Roboti aTokyo, JapancNovember 3-7 a3015 -302210aRobotics1 aFarnioli, E1 aGabiccini, M1 aBonilla, M1 aBicchi, A. uhttp://www.centropiaggio.unipi.it/publications/grasp-compliance-regulation-synergistically-controlled-robotic-hands-vsa.html