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_11 created an autonomous chemistry laboratory coupled with optimization algorithms to self-adjust the input parameters for given targets to derive new nucleation theories. A robot arm was used for object seeking and transferring of samples into the reaction chamber or the workstation. For liquid injection, a peristaltic pump that was installed on the reaction chamber was utilized.
@cite_11
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The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory fr... Robotic assistants have the potential to greatly improve our quality of life by supporting us in our daily activities. A service robot acting autonomously in an indoor environment is faced with very complex tasks. Consider the problem of pouring a liquid into a cup, the robot should first determine if the cup is empty or partially filled. RGB-D cameras provide noisy depth measurements which depend on the opaqueness and refraction index of the liquid. In this paper, we present a novel probabilistic approach for estimating the fill-level of a liquid in a cup using an RGB-D camera. Our approach does not make any assumptions about the properties of the liquid like its opaqueness or its refraction index. We develop a probabilistic model using features extracted from RGB and depth data. Our experiments demonstrate the robustness of our method and an improvement over the state of the art. Robot learning from demonstration faces new challenges when applied to tasks in which forces play a key role. Pouring liquid from a bottle into a glass is one such task, where not just a motion with a certain force profile needs to be learned, but the motion is subtly conditioned by the amount of liquid in the bottle. In this paper, the pouring skill is taught to a robot as follows. In a training phase, the human teleoperates the robot using a haptic device, and data from the demonstrations are statistically encoded by a parametric hidden Markov model, which compactly encapsulates the relation between the task parameter (dependent on the bottle weight) and the force-torque traces. Gaussian mixture regression is then used at the reproduction stage for retrieving the suitable robot actions based on the force perceptions. Computational and experimental results show that the robot is able to learn to pour drinks using the proposed framework, outperforming other approaches such as the classical hidden Markov models in that it requires less training, yields more compact encodings and shows better generalization capabilities. We present information theoretic search strategies for single and multi-robot teams to find and localize the source of a biochemical or radiological materials in turbulent flows. In our work, robots rely on sporadic and intermittent sensor readings to synthesize information maximizing exploration strategies to find and localize the position of the source. By reasoning about the spatial distribution of these sensory cues, the robots are able to construct a belief distribution over the possible positions of the source. The belief distribution is then employed to synthesize motion strategies that drives the robots to regions in the workspace that results in the largest decrease in the entropy of the belief distribution for the source position. We validate the proposed strategies in 2D and 3D environments and consider the performance of the strategies when robots have limited access to global pose information. In particular, the proposed strategies are validated using a three dimensional (3D) time-varying computational fluid model of the 2010 Deep Water Horizon oil spill.