Abstract: Robotic manipulation aims automatic generation of robot motion sequences for manipulation of movable objects among obstacles, to achieve a desired goal configuration. Some of these objects can only move when picked up by robots, and the order of pick-and-place operations for manipulation may matter to obtain a feasible kinematic solution. Therefore, geometric reasoning and motion planning alone are not sufficient to solve these manipulation problems; and planning of actions such as the pick-and-place operations need to be integrated with the motion planning problem. We present a modular framework that combines these two sorts of reasoning, using expressive formalisms and efficient solvers of answer set programming. We illustrate applications of this framework to complex robotic manipulation tasks that require concurrent execution of actions, with both dynamic simulations and physical implementations, using multiple Kuka youBots.

Bio: Esra Erdem is an associate professor in Computer Science and Engineering at Sabanci University. She received her Ph.D. in computer sciences at the University of Texas at Austin (2002), and visited University of Toronto and Vienna University of Technology for postdoctoral research (2002-2006). Her research is in the area of artificial intelligence, in particular, the mathematical foundations of knowledge representation and reasoning, and their applications to cognitive robotics and computational biology.

Abstract: There is a long tradition associating language and other serial cognitive behavior with an underlying motor planning mechanism (Piaget 1936, Lashley 1951, Miller et al. 1960, passim). The evidence is evolutionary, neurophysiological, and developmental. It suggests that language is much more closely related to embodied cognition than current linguistic theories of grammar suggest.

I'll argue that practically every aspect of language reflects this connection transparently. Building on planning formalisms developed in Robotics and AI, with some attention to applicable machine learning techniques, two basic operation corresponding to seriation and affordance will be shown to provide the basis for both plan-composition in animals, and long-range dependency in human language, of the kind found in constructions like relative clauses and coordination.

A connection this direct raises a further obvious question: If language is so closely related to animal planning, why don't any other animals have language? I'll further argue that the specific requirements of human collaborative planning, involving actions like helping and promising that depend on an understanding of other minds that has been found to be lacking in other animals, provides a distinctively semantic precursor for recursive aspects distinguishing human language from animal communication. I'll show that the automaton that is minimally necessary to conduct search for collaborative plans, which is of only slightly greater generality than the push-down automaton, is exactly the automaton that also appears to characterize the parsing problem for natural languages.

Bio: Mark Steedman is a Professor of Cognitive Science in Informatics at the University of Edinburgh, working in computational linguistics, artificial intelligence, the communicative use of prosody, tense and aspect, and wide-coverage parsing using Combinatory Categorial Grammar (CCG). Prof. Steedman is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Royal Society of Edinburgh (FRSE), and the British Academy (FBA). He is a member of the Academy of Europe and a former President of the Association for Computational Linguistics (ACL).