Interactive Co-Design of Form and Function for Legged Robots using the Adjoint Method

Our goal is to make robotics more accessible to casual users by reducing the domain knowledge required in designing and building robots. Towards this goal, we present an interactive computational design system that enables users to design legged robots with desired morphologies and behaviors by specifying higher level descriptions. The core of our method is a design optimization technique that reasons about the structure, and motion of a robot in coupled manner in order to achieve user-specified robot behavior, and performance. We are inspired by the recent works that also aim to jointly optimize robot's form and function. However, through efficient computation of necessary design changes, our approach enables us to keep user-in-the-loop for interactive applications. We evaluate our system in simulation by automatically improving robot designs for multiple scenarios. Starting with initial user designs that are physically infeasible or inadequate to perform the user-desired task, we show optimized designs that achieve user-specifications, all while ensuring an interactive design flow.Comment: 8 pages; added link of the accompanying vide

Automatic gauge detection via geometric fitting for safety inspection

For safety considerations in electrical substations, the inspection robots are recently deployed to monitor important devices and instruments with the presence of skilled technicians in the high-voltage environments. The captured images are transmitted to a data station and are usually analyzed manually. Toward automatic analysis, a common task is to detect gauges from captured images. This paper proposes a gauge detection algorithm based on the methodology of geometric fitting. We first use the Sobel filters to extract edges which usually contain the shapes of gauges. Then, we propose to use line fitting under the framework of random sample consensus (RANSAC) to remove straight lines that do not belong to gauges. Finally, the RANSAC ellipse fitting is proposed to find most fitted ellipse from the remaining edge points. The experimental results on a real-world dataset captured by the GuoZi Robotics demonstrate that our algorithm provides more accurate gauge detection results than several existing methods

Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters

Sequential reasoning is a complex human ability, with extensive previous research focusing on gaming AI in a single continuous game, round-based decision makings extending to a sequence of games remain less explored. Counter-Strike: Global Offensive (CS:GO), as a round-based game with abundant expert demonstrations, provides an excellent environment for multi-player round-based sequential reasoning. In this work, we propose a Sequence Reasoner with Round Attribute Encoder and Multi-Task Decoder to interpret the strategies behind the round-based purchasing decisions. We adopt few-shot learning to sample multiple rounds in a match, and modified model agnostic meta-learning algorithm Reptile for the meta-learning loop. We formulate each round as a multi-task sequence generation problem. Our state representations combine action encoder, team encoder, player features, round attribute encoder, and economy encoders to help our agent learn to reason under this specific multi-player round-based scenario. A complete ablation study and comparison with the greedy approach certify the effectiveness of our model. Our research will open doors for interpretable AI for understanding episodic and long-term purchasing strategies beyond the gaming community.Comment: 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20

Interactive Co-Design Of Form And Function For Legged Robots Using The Adjoint Method

Our goal is to make robotics more accessible to casual users by reducing the domain knowledge required in designing and building robots. Towards this goal, we present an interactive computational design system that enables users to design legged robots with desired morphologies and behaviors by specify- ing higher level descriptions. The core of our method is a design optimization technique that reasons about the structure and motion of a robot in a coupled manner to achieve user-speci ed robot behavior and performance. We are in- spired by the recent works that also aim to jointly optimize robot's form and function. However, through eficient computation of necessary design changes, our approach enables us to keep user-in-the-loop for interactive applications. We evaluate our system in simulation by starting with initial user designs that are physically infeasible or inadequate to perform the user-desired task. We then show optimized designs that achieve user-speci cations, all while ensur- ing an interactive design  ow

Production of spherical molecularly imprinted polymeric particles containing CO2-philic nanocavities

Production of spherical molecularly imprinted polymeric particles containing CO2-philic nanocavitie

Parametric investigation of CO2-MIPs production using suspension polymerisation method

Parametric investigation of CO2-MIPs production using suspension polymerisation metho

Production of functional porous polymeric particles with CO2 recognition properties and tuned morphology [Abstract]

Production of functional porous polymeric particles with CO2 recognition properties and tuned morphology [Abstract

Production of spherical mesoporous molecularly imprinted polymer particles containing tunable amine decorated nanocavities with CO2 molecule recognition properties

Novel spherical molecularly imprinted polymer (MIP) particles containing amide-decorated nanocavities with CO2 recognition properties in the poly[acrylamide-co-(ethyleneglycol dimethacrylate)] mesoporous matrix were synthesized by suspension polymerization using oxalic acid and acetonitrile/toluene as dummy template and porogen mixture, respectively. The particles had a maximum BET surface area, SBET, of 457 m2/g and a total mesopore volume of 0.92 cm3/g created by phase separation between the copolymer and porogenic solvents. The total volume of the micropores (d < 2 nm) was 0.1 cm3/g with two sharp peaks at 0.84 and 0.85 nm that have not been detected in non-imprinted polymer material. The degradation temperature at 5% weight loss was 240–255 °C and the maximum equilibrium CO2 adsorption capacity was 0.56 and 0.62 mmol/g at 40 and 25 °C, respectively, and 0.15 bar CO2 partial pressure. The CO2 adsorption capacity was mainly affected by the density of CO2-philic NH2 groups in the polymer network and the number of nanocavities. Increasing the content of low-polar solvent (toluene) in the organic phase prior to polymerization led to higher CO2 capture capacity due to stronger hydrogen bonds between the template and the monomer during complex formation. Under the same conditions, molecularly imprinted particles showed much higher CO2 capture capacity compared to their non-imprinted counterparts. The volume median diameter (73–211 μm) and density (1.3 g/cm3) of the produced particles were within the range suitable for CO2 capture in fixed and fluidized bed systems