Abstract
In its lifetime, a robot should be able to autonomously understand the semantics of different tasks to effectively perform them in different situations. In this context, it is important to distinguish the meaning (in terms of the desired effect) of a task and the means to achieve that task. Our focus is those tasks in which one agent is required to perform a task for another agent, such as give, show, hide, make-accessible, etc. In this paper, we identify that a high-level human-centered combined reasoning, based on perspective taking, efforts and abilities analyses, is the key to understand semantics of such tasks. By combining these aspects, the robot infers sets of hierarchy of facts, which serve for analyzing the effect of a task. We adapt the explanation based learning approach enabling the task understanding from the very first demonstration and continuous refinement with new demonstrations. We argue that such symbolic level understanding of a task, which is not bound to trajectory, kinematics structure or shape of the robot, facilitates generalization to novel situations as well as ease the transfer of acquired knowledge among heterogeneous robots. Further, the knowledge of tasks at such human understandable level of abstraction will enrich the natural human–robot interaction.
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Agostini A, Torras C, Wörgötter F (2011) Integrating task planning and interactive learning for robots to work in human environments. In: Proceedings of the 22nd international conference on artificial intelligence (IJCAI), pp 2386–2391
Alili S, Alami R, Montreuil V (2008) A task planner for an autonomous social robot. In: Distributed autonomous robotic systems (DARS), pp 335–344
Alili S, Pandey A, Sisbot EA, Alami R (2010) Interleaving symbolic and geometric reasoning for a robotic assistant. In: ICAPS workshop on combining action and motion planning (CAMP)
Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Robot Auton Syst 57(5):469–483
Awaad I, Kraetzschmar GK, Hertzberg J (2013) Affordance-based reasoning in robot task planning. In: Planning and robotics (PlanRob) workshop at 23rd international conference on automated planning and scheduling (ICAPS)
Breazeal C, Berlin M, Brooks AG, Gray J, Thomaz AL (2006) Using perspective taking to learn from ambiguous demonstrations. Robot Auton Syst 54:385–393
Calinon S, Dhalluin F, Caldwell D, Billard A (2009) Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework. In: 9th IEEE-RAS international conference on humanoid robots, humanoids 2009, pp 582–588
Cambon S, Alami R, Gravot F (2009) A hybrid approach to intricate motion, manipulation and task planning. Int J Robot Res 28(1):104–126
Cantrell R, Schermerhorn P, Scheutz M (2011) Learning actions from human-robot dialogues. In: IEEE RO-MAN, pp 125–130
Carello C, Grosofsky A, Reichel FD, Solomon HY, Turvey M (1989) Visually perceiving what is reachable. Ecol Psychol 1(1):27–54
Carpenter M, Call J (2002) The chemistry of social learning. Dev Sci 5(1):22–24
Cestnik B (1990) Estimating probabilities: a crucial task in machine learning. In: Proceedings of the ninth European conference on artificial intelligence, ECAI, pp 147–149
Chao C, Cakmak M, Thomaz A (2011) Towards grounding concepts for transfer in goal learning from demonstration. In: IEEE international conference on development and learning (ICDL) vol 2, pp 1–6
Chella A, Dindo H, Infantino I (2006) A cognitive framework for imitation learning. Robot Auton Syst 54(5):403–408
Choi HJ, Mark LS (2004) Scaling affordances for human reach actions. Hum Mov Sci 23(6):785–806
Dantam N, Essa I, Stilman M (2012) Linguistic transfer of human assembly tasks to robots. In: Proceedings of intelligent robots and systems (IROS), pp 237–242
de Silva L, Pandey AK, Alami R (2013) An interface for interleaved symbolic-geometric planning and backtracking. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 232–239
De Silva L, Gharbi M, Pandey AK, Alami R (2014) A new approach to combined symbolic-geometric backtracking in the context of human–robot interaction. In: IEEE international conference on robotics and automation (ICRA)
Dejong G, Mooney R (1986) Explanation-based learning: an alternative view. Mach Learn 1:145–176
Dillmann R (2004) Teaching and learning of robot tasks via observation of human performance. Robot Auton Syst 47:109–116
Dragan A, Gordon G, Srinivasa S (2011) Learning from experience in manipulation planning: setting the right goals. In: Proceedings of the international symposium on robotics research (ISRR)
Ekvall S, Kragic D (2008) Robot learning from demonstration: a task-level planning approach. Int J Adv Robot Syst 5(3):223–234
Flann NS, Dietterich GT (1989) A study of explanation-based methods for inductive learning. Mach Learn 4:187–226
Flavell JH, Everett BA, Croft K, Flavell ER (1981) Young children’s knowledge about visual perception: further evidence for the level 1–level 2 distinction, pp 99–103
Flavell JH, Shipstead SG, Croft K (1978) Young children’s knowledge about visual perception: Hiding objects from others. Child Dev 49(4):1208–1211
Furnkranz J, Flach P (2003) An analysis of rule evaluation metrics. In: Proceedings of the 20th international conference on machine learning (ICML). AAAI Press, pp 202–209
Gardner DL, Mark LS, Ward JA, Edkins H (2001) How do task characteristics affect the transitions between seated and standing reaches? Ecol Psychol 13(4):245–274
Gribovskaya E, Khansari-Zadeh S, Billard A (2011) Learning non-linear multivariate dynamics of motion in robotic manipulators. Int J Robot Res 30(1):80–117
Hopper LM, Lambeth SP, Schapiro SJ, Whiten A (2008) Observational learning in chimpanzees and children studied through ghostconditions. Proc R Soc B Biol Sci 275(1636):835–840
Johnson M, Demiris Y (2005) Perceptual perspective taking and action recognition. Int J Soc Robot 2:181–199
Jkel R, Schmidt-Rohr S, Rhl S, Kasper A, Xue Z, Dillmann R (2012) Learning of planning models for dexterous manipulation based on human demonstrations. Int J Soc Robot 4(4):437–448
Khatib O, Demircan E, Sapio VD, Sentis L, Besier T, Delp S (2009) Robotics-based synthesis of human motion. J Physiol Paris 103:211–219
Kuniyoshi Y, Inaba M, Inoue H (1994) Learning by watching: extracting reusable task knowledge from visual observation of human performance. IEEE Trans Robot Autom 10(6):799–822
Lallee S, Lemaignan S, Lenz A, Melhuish C, Natale L, Skachek S, van Der Zant T, Warneken F, Dominey PF (2010) Towards a platform-independent cooperative human–robot interaction system: I. perception. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 4444–4451
Lee KH, Lee J, Thomaz AL, Bobick AF (2009) Effective robot task learning by focusing on task-relevant objects. In: Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems. IEEE Press, Piscataway, NJ, USA, pp 2551–2556
Lemaignan S, Ros R, Mösenlechner L, Alami R, Beetz M (2010) Oro, a knowledge management module for cognitive architectures in robotics. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 3548–3553
Lempers JD, Flavell ER, Flavell JH (1977) The development in very young children of tacit knowledge concerning visual perception. Genet Psychol Monogr 95(1):3–53
Levas A, Selfridge M (1984) A user-friendly high-level robot teaching system. In: IEEE international conference on robotics and automation. proceedings, vol 1, pp 413–416
Lopes M, Melo FS, Montesano L (2007) Affordance-based imitation learning in robots. In: IROS, pp 1015–1021
Lunsky LL (1965) Learning and instinct in animals. Arch Intern Med 115(6):757–758
Marin-Urias L, Sisbot E, Pandey A, Tadakuma R, Alami R (2009) Towards shared attention through geometric reasoning for human robot interaction. In: 9th IEEE-RAS international conference on humanoid robots, humanoids 2009, pp 331–336
Michael L (2011) Causal learnability. In: Barcelona, International joint conference on artificial intelligence (IJCAI), pp 1014–1020
Montesano L, Lopes M, Bernardino A, Santos-Victor J (2007) Modeling affordances using bayesian networks. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 4102–4107
Muhlig M, Gienger M, Hellbach S, Steil JJ, Goerick C (2009) Task-level imitation learning using variance-based movement optimization. In: IEEE international conference on robotics and automation, pp 1177–1184
Nielsen M (2006) Copying actions and copying outcomes: social learning through the second year. Dev Psychol 42(3):555
Ogawara K, Takamatsu J, Kimura H, Ikeuchi K (2003) Extraction of essential interactions through multiple observations of human demonstrations. IEEE Trans Ind Electron 50(4):667–675
Pandey AK, Alami R (2010) Mightability maps: a perceptual level decisional framework for co-operative and competitive human-robot interaction. In: International conference on intelligent robots and systems (IROS) year 2010, pp 5842–5848
Pandey AK, Alami R (2013) Affordance graph: a framework to encode perspective taking and effort based affordances for day-to-day human–robot interaction. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 2180–2187
Pandey AK, Alami R (2014) Ingredients and a framework of dexterous manipulation skills for robots in human centered environment and hri. J Robot Soc Jpn 32(4):31–37
Pandey AK, Ali M, Alami R (2013) Towards a task-aware proactive sociable robot based on multi-state perspective-taking. Int J Soc Robot 5(2):215–236
Pandey AK, Saut JP, Sidobre D, Alami R (2012) Towards planning human-robot interactive manipulation tasks: task dependent and human oriented autonomous selection of grasp and placement. In: Proceedings of IEEE RAS & EMBS BioRob, pp 1371–1376
Pardowitz M, Dillmann R (2007) Towards life-long learning in household robots: the piagetian approach. In: IEEE 6th international conference on development and learning (ICDL), pp 88–93
Pasula H, Zettlemoyer L, Kaelbling L (2004) Learning probabilistic relational planning rules. In: International conference on automated planning and scheduling (ICAPS), pp 73–82
Piaget J (1945) Play, dreams, and imitation in childhood. Norton, New York
Ros R, Lemaignan S, Sisbot EA, Alami R, Steinwender J, Hamann K, Warneken F (2010) Which one? grounding the referent based on efficient human–robot interaction. In: 19th IEEE international symposium in robot and human interactive communication, pp 570–575
Sapio V, Warren J, Khatib O (2006) Predicting reaching postures using a kinematically constrained shoulder model. In: Lennarcic J, Roth B (eds) Advances in robot kinematics. Springer, Berlin, pp 209–218
Saut JP, Sidobre D (2012) Efficient models for grasp planning with a multi-fingered hand. Robot Auton Syst 60(3):347–357
Schmidt-Rohr S, Losch M, Dillmann R (2010) Learning flexible, multi-modal human–robot interaction by observing human–human–interaction. In: IEEE RO-MAN, pp 582–587
Simeon T, p. Laumond J, Lamiraux F (2001) Move3d: a generic platform for path planning. In: 4th international symposium on assembly and task planning, pp 25–30
Tenorth M, Beetz M (2009) Knowrob- knowledge processing for autonomous personal robots. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 4261–4266
Tomasello M (1990) Cultural transmission in the tool use and communicatory signaling of chimpanzees?. Cambridge University Press, Cambridge, MA, pp 274–311
Trafton J, Cassimatis N, Bugajska M, Brock D, Mintz F, Schultz A (2005) Enabling effective human–robot interaction using perspective-taking in robots. Syst Man Cybern Part A Syst Hum IEEE Trans 35(4):460–470
Weber BG, Mateas M, Jhala A (2012) Learning from demonstration for goal-driven autonomy. In: AAAI
Wood DJ (1998) How children think and learn: the social contexts of cognitive development. Blackwell, Oxford
Wusteman J (1992) Explanation-based learning: a survey. Artif Intell Rev 6:243–262
Ye G, Alterovitz R (2011) Demonstration-guided motion planning. In: Proceedings of the international symposium on robotics research (ISRR)
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This work is partially conducted within Romeo2 (Humanoid Robot Assistant and Companion for Everyday Life) project (http://projetromeo.com/), Funded by BPIFrance in the framework of the Structuring Projects of Competitiveness Clusters (PSPC).
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Pandey, A.K., Alami, R. Towards Human-Level Semantics Understanding of Human-Centered Object Manipulation Tasks for HRI: Reasoning About Effect, Ability, Effort and Perspective Taking. Int J of Soc Robotics 6, 593–620 (2014). https://doi.org/10.1007/s12369-014-0246-y
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DOI: https://doi.org/10.1007/s12369-014-0246-y