We present the system overview and integration of the ASIMO autonomous robot that can function successfully in indoor environments. The first model of ASIMO is already being leased to companies for receptionist work.In this paper, we describe the new capabilities that we have added to ASIMO. We explain the structure of the robot system for intelligence, integrated subsystems on its body, and their new functions. We describe the behavior-based planning architecture on ASIMO and its vision and auditory system. We describe its gesture recognition system, human interaction and task performance. We also discuss the external online database system that can be accessed using internet to retrieve desired information, the management system for receptionist work, and various function demonstrations. 0-7803-739&7lOU$17.00 WOO2 IEEE

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We report superconductivity in the novel 112-type iron-based compound Ca 1Àx La x FeAs 2 . Single-crystal X-ray diffraction analysis revealed that the compound crystallizes in a monoclinic structure (space group P2 1 ), in which Fe 2 As 2 layers alternate with Ca 2 As 2 spacer layers such that monovalent arsenic forms zigzag chains. Superconductivity with a transition temperature (T c ) of 34 K was observed for the x ¼ 0:1 sample, while the x ¼ 0:21 sample exhibited trace superconductivity at 45 K. First-principles band calculations demonstrated the presence of almost cylindrical Fermi surfaces, favorable for the high T c in La-doped CaFeAs 2 .KEYWORDS: iron-based superconductors, Ca-La-Fe-As, 112-type, CaFeAs 2Since the discovery of superconductivity with a transition temperature (T c ) of 26 K in LaFeAsO 1Àx F x , 1) there has been tremendous effort towards synthesizing novel iron pnictide superconductors. [2][3][4][5][6][7][8][9][10][11][12][13][14] All of the iron pnictide superconductors identified so far consist of a common structural motif, i.e., Fe 2 As 2 layers that are alternately stacked with various kinds of spacer layers. Therefore, the central goal for realizing a higher T c has been finding a novel spacer layer that can suitably tune the electronic states of Fe 2 As 2 layers.Recently, superconductivity has been discovered in Ca 10 (Pt n As 8 )(Fe 2Àx Pt x As 2 ) 5 , which consists of As-As dimers with a formal electron count of As 2À in the spacer layer. [15][16][17][18] Because of the 4p 3 electron configuration of elemental arsenic, arsenic can form various bonding structures: (i) Isolated arsenic with a formal electron count of As 3À . Examples include A 3 As (A ¼ Li, Na, and K) and iron-based superconductors. (ii) Dimerized As-As with a single bond. Its formal electron count is As 2À . Sr 2 As 2 and Ca 10 (Pt n As 8 )(Fe 2Àx Pt x As 2 ) 5 with As-As dimer bonds in the spacer layer can be categorized here. (iii) A one-dimensional chain connected by arsenic single bonds with a formal electron count of As À . This category includes KAs as an example. Realizing novel iron-based superconductors with spacer layers composed of complex bonding networks of arsenic such as (iii) has been a longstanding challenge: Shim et al. have theoretically proposed the hypothetical compound BaFeAs 2 (112-type) with spacer layers of the arsenic square network, and suggested that such compounds can be used to examine the role of charge and polarization fluctuations as well as the importance of two-dimensionality in the mechanism of superconductivity. 19) Although the 112-type iron pnictides AEFeAs 2 (AE ¼ Ca, Sr, Ba) have not yet been synthesized, the isostructural compounds RET As 2 (RE = rare-earth elements; T ¼ Cu, Ag, Au) have been studied intensively. 20,21) In this letter, we present a report on the novel 112-type iron-based superconductor Ca 1Àx La x FeAs 2 . Although pure CaFeAs 2 was not obtained, we found that the substitution of a small amount of La for Ca stabilizes the 112 phase. Thus, Ca 1Àx La x FeAs 2 ...

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.