Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.     

Molecular dynamics simulations of diffusion of submonolayer polar liquid lubricant films on solid surfaces

A fundamental understanding of the diffusion phenomena of submonolayer polar liquid films is important for achieving reliable lubrication between moving mechanical parts separated by a nanometer-sized gap. To acquire this understanding, we conducted molecular dynamics (MD) simulations of diffusion phenomena of submonolayer polar perfluoropolyether (PFPE) Zdol films on solid surfaces. To improve the accuracy of these simulations, we developed an all-atom model that includes hydrogen-bond potential and refined atomic charges for Zdol molecules and tested it through MD simulations of spreading of step-shaped submonolayer PFPE films. Our MD simulations reproduced the experimentally observed effects of polar end groups on the diffusion speed and molecular conformation of Zdol. We then conducted MD simulations of self-diffusion of submonolayer Zdol films; these simulations demonstrated that as the thickness of the submonolayer Zdol films decreases, molecular conformation becomes flatter and the self-diffusion coefficient decreases. These changes in molecular conformation partially explain our experimental finding that the spreading of step-shaped submonolayer polar PFPE films slows down with decreasing initial thickness.     

Achieving evenhandedness in certified email system for contract signing

Certified email is a system which enables a sender to prove a receiver’s receipt of email. Such a system can be used for applications related to electronic commerce on the Internet. This paper considers a situation where a sender or a receiver wants to change his/her mind due to the change of mail content value (e.g., stock, auction, gambling) during the transaction. We point out that no traditional certified email systems have been designed for such a case, thus one of the participants can be at a disadvantage. To avoid this problem, we propose an evenhanded certified email system in which each participant can change his/her choice, either cancel or finish the transaction, at any time during the transaction.

Technique for anisotropic extension of organic crystals: application to temperature dependence of electrical resistance

We have developed a technique for the anisotropic extension of fragile molecular crystals. The pressure medium and the instrument, which extends the pressure medium, are both made from epoxy resin. Since the thermal contraction of our instrument is identical to that of the pressure medium, the strain applied to the pressure medium has no temperature dependence down to 2 K. Therefore, the degree of extension applied to the single crystal at low temperatures is uniquely determined from the degree of extension in the pressure medium and thermal contractions of the epoxy resin and the single crystal at ambient pressure. Using this novel instrument, we have measured the temperature dependence of the electrical resistance of metallic, superconducting, and insulating materials. The experimental results are discussed from the viewpoint of the extension (compression) of the lattice constants along the parallel (perpendicular) direction.     

Selectivity and stability via dendritic nonlinearity

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.     

Patient views of adverse events: comparisons of self-reported healthcare staff attitudes with disclosure of accident information

In the present paper, we report results of surveys in 2003 in Japan and Denmark about patients' views about adverse events, focusing on the actions of healthcare staff involved in a medical accident. Results show that patients were more likely to indicate negative expectations to a doctor's reactions after a medical accident when asked in general terms than when asked in relation to concrete case stories. When asked in general terms, 66% (47%) of Japanese (Danish) respondents expected that doctors sometimes hold back on providing information to patients about a medical accident, while 37% (7%) did so when asked about a concrete, mild-outcome case. We examine some possible reasons for the relatively high level of distrust of Japanese patients, and we discuss whether the seemingly lower level of disclosure in Japan than in Denmark and the negative stories in the Japanese press may have an impact. We also suggest some implications for introducing a patient-centred or customer-centred approach to risk management in healthcare and other domains.     

Further investigation on the thermal decomposition of aluminium dialkylamides

Both dimethylamino- (I) and diethylamino-alane dimers (II), [(R₂N)₂AlH]₂ (I, R=Me; II, R=Et) decompose above 800° C under a few hundred Pa of hydrogen to dark greyish, hard (Vickers hardness larger than 2000), oxidation-resistive and oxidation-protective deposits which are tentatively identified as Al₅C₃N. The deposits are stable to moisture and diluted hydrochloric acid in contrast to those obtained below 800° C, but they easily dissolve in sodium hydroxide solution at room temperature evolving a gas. The deposits on a stainless steel substrate adhere strongly to the substrate and remain so on rapid heating and cooling. The electrical resistivity of the deposits is in the range 10² to 10⁴ cm.     

High rate nitrogen removal by the CANON process at ambient temperature

Completely autotrophic nitrogen removal over nitrite (CANON) is a cost-effective nitrogen removal process. Implementation of the CANON process relies on the cooperation of ammonium-oxidizing and Anammox bacteria, as well as the inhibition of nitrite-oxidizing bacteria. Strict limitations on dissolved oxygen (DO) concentration in the reactor, and the addition of sufficient inorganic carbon in the influent, were adopted as the main operational strategies. The reactor was fed with synthetic inorganic wastewater composed mainly of NH(4)(+)-N, and operated for 106 days. Stable nitrogen removal rates (NRR) of around 1.4 kg N m(-3) d(-1) were obtained at ambient temperature. Morphological characteristics and analysis of bacterial community confirmed the formation of functional outer aerobic and inner anaerobic granular sludge, providing evidence of stable nitrogen removal.     

MOSAIC for multiple-reward environments

Reinforcement learning (RL) can provide a basic framework for autonomous robots to learn to control and maximize future cumulative rewards in complex environments. To achieve high performance, RL controllers must consider the complex external dynamics for movements and task (reward function) and optimize control commands. For example, a robot playing tennis and squash needs to cope with the different dynamics of a tennis or squash racket and such dynamic environmental factors as the wind. In addition, this robot has to tailor its tactics simultaneously under the rules of either game. This double complexity of the external dynamics and reward function sometimes becomes more complex when both the multiple dynamics and multiple reward functions switch implicitly, as in the situation of a real (multi-agent) game of tennis where one player cannot observe the intention of her opponents or her partner. The robot must consider its opponent's and its partner's unobservable behavioral goals (reward function). In this article, we address how an RL agent should be designed to handle such double complexity of dynamics and reward. We have previously proposed modular selection and identification for control (MOSAIC) to cope with nonstationary dynamics where appropriate controllers are selected and learned among many candidates based on the error of its paired dynamics predictor: the forward model. Here we extend this framework for RL and propose MOSAIC-MR architecture. It resembles MOSAIC in spirit and selects and learns an appropriate RL controller based on the RL controller's TD error using the errors of the dynamics (the forward model) and the reward predictors. Furthermore, unlike other MOSAIC variants for RL, RL controllers are not a priori paired with the fixed predictors of dynamics and rewards. The simulation results demonstrate that MOSAIC-MR outperforms other counterparts because of this flexible association ability among RL controllers, forward models, and reward predictors.