This paper proposes a novel type of grasping strategy that draws inspiration from the role of touch and the importance of wrist motions in human grasping. The proposed algorithm, which we call Sequential Contact-based Adaptive Grasping, can be used to reactively modify a given grasp plan according to contacts arising between the hand and the object. This technique, based on a systematic constraint categorization and an iterative task inversion procedure, is shown to lead to synchronized motions of the fingers and the wrist, as it can be observed in humans, and to increase grasp success rate by substantially mitigating the relevant problems of object slippage during hand closure and of uncertainties caused by the environment and by the perception system. After describing the grasping problem in its quasi-static aspects, the algorithm is derived and discussed with some simple simulations. The proposed method is general as it can be applied to different kinds of robotic hands. It refines a priori defined grasp plans and significantly reduces their accuracy requirements by relying only on a forward kinematic model and elementary contact information. The efficacy of our approach is confirmed by experimental results of tests performed on a collaborative robot manipulator equipped with a state-of-the-art underactuated soft hand.
Al-IbadiANefti-MezianiSDavisS (2018) Active soft end effectors for efficient grasping and safe handling. IEEE Access6: 23591–23601.
2.
AmbikeSPacletFZatsiorskyVM, et al. (2014) Factors affecting grip force: anatomy, mechanics, and referent configurations. Experimental Brain Research232(4): 1219–1231.
3.
BaeJHParkSWKimD, et al. (2012) A grasp strategy with the geometric centroid of a groped object shape derived from contact spots. In: 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, 14–18 May 2012, pp. 3798–3804, Piscataway, NJ. IEEE.
4.
BianchiMAvertaGBattagliaEet al. (2018) Touch-based grasp primitives for soft hands: applications to human-to-robot handover tasks and beyond. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 21-25 May 2018, pp. 7794–7801, Piscataway, NJ. IEEE,.
5.
BicchiA (1994) On the problem of decomposing grasp and manipulation forces in multiple whole-limb manipulation. Robotics and Autonomous Systems13(2): 127–147.
6.
BicchiA (1995) On the closure properties of robotic grasping. The International Journal of Robotics Research14(4): 319–334.
7.
BicchiAGabicciniMSantelloM (2011) Modelling natural and artificial hands with synergies. Philosophical Transactions of the Royal Society B: Biological Sciences366(1581): 3153–3161.
8.
BierbaumARambowMAsfourT, et al (2009) Grasp affordances from multi-fingered tactile exploration using dynamic potential fields. In: Humanoid Robots, 2009. Humanoids 2009. 9th IEEE-RAS International Conference on, Paris, France, 7-10 December 2009, pp. 168–174, Piscataway, NJ. IEEE.
9.
BohgJMoralesAAsfourT, et al. (2014) Data-driven grasp synthesis-a survey. IEEE Transactions on Robotics30(2): 289–309.
10.
BussMHashimotoHMooreJB (1996) Dextrous hand grasping force optimization. IEEE Transactions on Robotics and Automation12(3): 406–418.
11.
CatalanoMGGrioliGFarnioliE, et al. (2014) Adaptive synergies for the design and control of the pisa/iit softhand. The International Journal of Robotics Research33(5): 768–782.
12.
CaumesMDe MonsabertBGHauraixH, et al. (2019) complex couplings between joints, muscles and performance: the role of the wrist in grasping. Scientific Reports9(1): 1–11.
13.
CutkoskyMRKaoI (1989) Computing and controlling compliance of a robotic hand. IEEE Transactions on Robotics and Automation5(2): 151–165.
14.
DangHAllenPK (2014) Stable grasping under pose uncertainty using tactile feedback. Autonomous Robots36(4): 309–330.
15.
DeimelRBrockO (2013) A compliant hand based on a novel pneumatic actuator. In: Robotics and Automation (ICRA), 2013 IEEE International Conference on, Karlsruhe, Germany, 6–10 May 2013, pp. 2047–2053. Piscataway, NJ. IEEE.
16.
DeimelRBrockO (2016) A novel type of compliant and underactuated robotic hand for dexterous grasping. The International Journal of Robotics Research35(1–3): 161–185.
17.
Della SantinaCBianchiMGrioliG, et al. (2017) Controlling soft robots: balancing feedback and feedforward elements. IEEE Robotics & Automation Magazine24(3): 75–83.
18.
DollarAMHoweRD (2010) The highly adaptive sdm hand: design and performance evaluation. The International Journal of Robotics Research29(5): 585–597.
19.
EkvallSKragicD (2004) Interactive grasp learning based on human demonstration. In: IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004, New Orleans, LA, 26 April–1 May 2004, Vol. 4, pp. 3519–3524, Piscataway, NJ. IEEE,.
20.
EppingerSSeeringW (1987) Introduction to dynamic models for robot force control. IEEE Control Systems Magazine7(2): 48–52.
21.
EppnerCDeimelRAlvarez-RuizJ, et al (2015) Exploitation of environmental constraints in human and robotic grasping. The International Journal of Robotics Research34(7): 1021–1038.
22.
FanSGuHZhangY, et al. (2018) Research on adaptive grasping with object pose uncertainty by multi-fingered robot hand. International Journal of Advanced Robotic Systems15(2): 1729881418766783.
23.
FarnioliEGabicciniMBicchiA (2016) Quasi-static analysis of synergistically underactuated robotic hands in grasping and manipulation tasks. In: Human and Robot Hands. Berlin, Germany: Springer, 211–233.
24.
FerrariCCannyJ (1992) Planning optimal grasps. In: Robotics and Automation, 1992. Proceedings., 1992 IEEE International Conference on, Nice, France, 12–14 May 1992, pp. 2290–2295, Piscataway, NJ. IEEE.
25.
FlaccoFDe LucaA (2014) A reverse priority approach to multi-task control of redundant robots. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, 14–18 September 2014, pp. 2421–2427, Piscataway, NJ. IEEE.
26.
FlanaganJRBowmanMCJohanssonRS (2006) Control strategies in object manipulation tasks. Current Opinion in Neurobiology16(6): 650–659.
27.
FriedlWHöppnerHSchmidtF, et al (2018) Clash: compliant low cost antagonistic servo hands. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1–5 October 2018, pp. 6469–6476, Piscataway, NJ. IEEE.
28.
HoganFRBauzaMCanalO, et al (2018) Tactile regrasp: grasp adjustments via simulated tactile transformations. arXiv Preprint.arXiv:1803.01940.
29.
HoweRDPoppNAkellaP, et al. (1990) Grasping, manipulation, and control with tactile sensing. In: Proceedings, IEEE International Conference on Robotics and Automation, Cincinnati, OH, 13–18 May 1990, pp. 1258–1263, Piscataway, NJ. IEEE.
30.
HsiaoKChittaSCiocarlieM, et al (2010) Contact-reactive grasping of objects with partial shape information. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 18–22 Octpber 2010, pp. 1228–1235, Piscataway, NJ. IEEE.
31.
JohanssonRSWestlingG (1991) Afferent signals during manipulative tasks in humans. In: Information Processing in the Somatosensory System. Berlin, Germany: Springer, 25–48.
32.
KappassovZCorralesJAPerdereauV (2015) Tactile sensing in dexterous robot hands. Robotics and Autonomous Systems74: 195–220.
33.
KerrJRothB (1986) Analysis of multifingered hands. The International Journal of Robotics Research4(4): 3–17.
34.
KrahnJMFabbroFMenonC (2017) A soft-touch gripper for grasping delicate objects. IEEE/ASME Transactions on Mechatronics22(3): 1276–1286.
35.
KronanderKBillardA (2016) Stability considerations for variable impedance control. IEEE Transactions on Robotics32(5): 1298–1305.
36.
KumarVRWaldronKJ (1988) Force distribution in closed kinematic chains. IEEE Journal on Robotics and Automation4(6): 657–664.
37.
LiZSastrySS (1988) Task-oriented optimal grasping by multifingered robot hands. IEEE Journal on Robotics and Automation4(1): 32–44.
38.
MaSFeldmanA (1995) Two functionally different synergies during arm reaching movements involving the trunk. Journal of Neurophysiology73(5): 2120–2122.
39.
MahlerJLiangJNiyazS, et al (2017) Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. arXiv Preprint arXiv:1703.09312.
40.
MahlerJPatilSKehoeB, et al (2015) Gp-gpis-opt: grasp planning with shape uncertainty using gaussian process implicit surfaces and sequential convex programming. In: 2015 IEEE international conference on robotics and automation (ICRA), Seattle, WA, 26–30 May 2015, pp. 4919–4926, Piscataway, NJ. IEEE.
41.
MahlerJPokornyFTHouB, et al. (2016) Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards. In: Robotics and Automation (ICRA), 2016 IEEE International Conference on, Stockholm, Sweden, 16–21 May 2016, pp. 1957–1964, Piscataway, NJ. IEEE.
42.
MarkenscoffXNiLPapadimitriouCH (1990) The geometry of grasping. The International Journal of Robotics Research9(1): 61–74.
43.
MontagnaniFControzziMCiprianiC (2015) Is it finger or wrist dexterity that is missing in current hand prostheses?IEEE Transactions on Neural Systems and Rehabilitation Engineering23(4): 600–609.
44.
NataleLTorres-JaraE (2006) A sensitive approach to grasping. In: Proceedings of the sixth international workshop on epigenetic robotics, Paris, France, 20 – 22 September 2006. pp. 87–94.
45.
NguyenV-D (1988) Constructing force-closure grasps. The International Journal of Robotics Research7(3): 3–16.
46.
OdhnerLUMaRRDollarAM (2013) Open-loop precision grasping with underactuated hands inspired by a human manipulation strategy. IEEE Transactions on Automation Science and Engineering10(3): 625–633.
47.
OlssonHÅströmKJDe WitCC, et al. (1998) Friction models and friction compensation. European Journal of Control4(3): 176–195.
48.
PerkinsPHeberS (2018) Identification of ribosome pause sites using a z-score based peak detection algorithm. In: 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Las Vegas, NV, 18–20 October 2018, pp. 1–6, Piscataway, NJ. IEEE.
49.
PrattichizzoDTrinkleJC (2016) Grasping. In: SicilianoBKhatibO (eds) Springer Handbook of Robotics. Berlin, Germany: Springer, Chap. 28, pp. 671–700.
50.
RomeroJKjellstromHKragicD (2009) Modeling and evaluation of human-to-robot mapping of grasps. In: 2009 International Conference on Advanced Robotics, Munich, Germany, 22–26 June 2009, pp. 1–6, Piscataway, NJ. IEEE.
51.
SahbaniAEl-KhourySBidaudP (2012) An overview of 3d object grasp synthesis algorithms. Robotics and Autonomous Systems60(3): 326–336.
52.
SantelloMFlandersMSoechtingJF (2002) Patterns of hand motion during grasping and the influence of sensory guidance. Journal of Neuroscience22(4): 1426–1435.
53.
SaxenaADriemeyerJNgAY (2008) Robotic grasping of novel objects using vision. The International Journal of Robotics Research27(2): 157–173.
54.
SicilianoBSlotineJJ (1991) A general framework for managing multiple tasks in highly redundant robotic systems. In: proceeding of 5th International Conference on Advanced Robotics, Pisa, Italy, 19–22 June 1991, vol. 2. pp. 1211–1216Piscataway, NJ. IEEE.
55.
SuYWuYLeeK, et al. (2012) Robust grasping for an under-actuated anthropomorphic hand under object position uncertainty. In: 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), Osaka, Japan, 29 November–1 December 2012, pp. 719–725, Piscataway, NJ. IEEE.
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