Automated chemical synthesis carries great promises of safety, efficiency and reproducibility for both research and industry laboratories. Current approaches are based on specifically-designed automation systems, which present two major drawbacks: (i) existing apparatus must be modified to be integrated into the automation systems; (ii) such systems are not flexible and would require substantial re-design to handle new reactions or procedures. In this paper, we propose a system based on a robot arm which, by mimicking the motions of human chemists, is able to perform complex chemical reactions without any modifications to the existing setup used by humans. The system is capable of precise liquid handling, mixing, filtering, and is flexible: new skills and procedures could be added with minimum effort. We show that the robot is able to perform a Michael reaction, reaching a yield of 34 , which is comparable to that obtained by a junior chemist (undergraduate student in Chemistry).
@cite_4 developed a Robot Scientist to automate early stages of drug design, by randomly screening a subset of its library to filter suitable compounds and use AI to hypothesize Quantitative Structure Activity Relationship (QSAR). Resembling @cite_11 and @cite_8 , the system included robot arms and they were used to transfer plates between different pieces of laboratory automation equipment.
@cite_8 @cite_4 @cite_11
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There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist ‘Eve’ designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax. Rapidly expanding internet resources and wireless networking have potential to liberate robots and automation systems from limited onboard computation, memory, and software. “Cloud Robotics” describes an approach that recognizes the wide availability of networking and incorporates open-source elements to greatly extend earlier concepts of “Online Robots” and “Networked Robots”. In this paper we consider how cloud-based data and computation can facilitate 3D robot grasping. We present a system architecture, implemented prototype, and initial experimental data for a cloud-based robot grasping system that incorporates a Willow Garage PR2 robot with onboard color and depth cameras, Google's proprietary object recognition engine, the Point Cloud Library (PCL) for pose estimation, Columbia University's GraspIt! toolkit and OpenRAVE for 3D grasping and our prior approach to sampling-based grasp analysis to address uncertainty in pose. We report data from experiments in recognition (a recall rate of 80 for the objects in our test set), pose estimation (failure rate under 14 ), and grasping (failure rate under 23 ) and initial results on recall and false positives in larger data sets using confidence measures. We address the problem of a mobile manipulation robot searching for an object in a cluttered domain that is populated with an unknown number of objects in an unknown arrangement. The robot must move around its environment, looking in containers, moving occluding objects to improve its view, and reasoning about collocation of objects of different types, all in service of finding a desired object. The key contribution in reasoning is a Markov-chain Monte Carlo (MCMC) method for drawing samples of the arrangements of objects in an occluded container, conditioned on previous observations of other objects as well as spatial constraints. The key contribution in planning is a receding-horizon forward search in the space of distributions over arrangements (including number and type) of objects in the domain; to maintain tractability the search is formulated in a model that abstracts both the observations and actions available to the robot. The strategy is shown empirically to improve upon a baseline systematic search strategy, and sometimes outperforms a method from previous work. This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC.