John Anderson

NeurIPS Conference 2023 Conference Paper

Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models

• Zhong Yi Wan
• Ricardo Baptista
• Anudhyan Boral
• Yi-Fan Chen
• John Anderson
• Fei Sha
• Leonardo Zepeda-Núñez

We introduce a two-stage probabilistic framework for statistical downscaling using unpaired data. Statistical downscaling seeks a probabilistic map to transform low-resolution data from a biased coarse-grained numerical scheme to high-resolution data that is consistent with a high-fidelity scheme. Our framework tackles the problem bycomposing two transformations: (i) a debiasing step via an optimal transport map, and (ii) an upsampling step achieved by a probabilistic diffusion model with a posteriori conditional sampling. This approach characterizes a conditional distribution without needing paired data, and faithfully recovers relevant physical statistics from biased samples. We demonstrate the utility of the proposed approach on one- and two-dimensional fluid flow problems, which are representative of the core difficulties present in numerical simulations of weather and climate. Our method produces realistic high-resolution outputs from low-resolution inputs, by upsampling resolutions of $8\times$ and $16\times$. Moreover, our procedure correctly matches the statistics of physical quantities, even when the low-frequency content of the inputs and outputs do not match, a crucial but difficult-to-satisfy assumption needed by current state-of-the-art alternatives. Code for this work is available at: https: //github. com/google-research/swirl-dynamics/tree/main/swirl_dynamics/projects/probabilistic_diffusion.

NeurIPS Conference 2023 Conference Paper

Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations

• Anudhyan Boral
• Zhong Yi Wan
• Leonardo Zepeda-Núñez
• James Lottes
• Qing Wang
• Yi-Fan Chen
• John Anderson
• Fei Sha

We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differential equations (SDE) for stochastic modeling. The ideal LES models the LES flow by treating each full-order trajectory as a random realization of the underlying dynamics, as such, the effect of small-scales is marginalized to obtain the deterministic evolution of the LES state. However, ideal LES is analytically intractable. In our work, we use a latent neural SDE to model the evolution of the stochastic process and an encoder-decoder pair for transforming between the latent space and the desired ideal flow field. This stands in sharp contrast to other types of neural parameterization of closure models where each trajectory is treated as a deterministic realization of the dynamics. We show the effectiveness of our approach (niLES – neural ideal LES) on two challenging chaotic dynamical systems: Kolmogorov flow at a Reynolds number of 20, 000 and flow past a cylinder at Reynolds number 500. Compared to competing methods, our method can handle non-uniform geometries using unstructured meshes seamlessly. In particular, niLES leads to trajectories with more accurate statistics and enhances stability, particularly for long-horizon rollouts. (Source codes and datasets will be made publicly available. )

KER Journal 2019 Journal Article

A sketch drawing humanoid robot using image-based visual servoing

• Meng-Cheng Lau
• John Anderson
• Jacky Baltes

Abstract This paper presents our sketch drawing artist humanoid robot research. One of the limitations of the existing artist humanoid robot is the lack of feedback on the error that occurs during the drawing process. The contribution of this research is the development of a humanoid robot artist with drawing error correction capability. Based on our previous work with open-loop control pen-and-ink humanoid robot artist, we have implemented a closed-loop visual servoing approach to address this problem. Our experimental results show that this approach is sufficient to correct drawing errors that occur due to mechanical limitation of a robot.

KER Journal 2017 Journal Article

Active Balancing and Turning for Alpine Skiing Robots – ADDENDUM

• Jacky Baltes
• Kuo-Yang Tu
• Soroush Sadeghnejad
• John Anderson

IS Journal 2017 Journal Article

Interactive Task Learning

• John E. Laird
• Kevin Gluck
• John Anderson
• Kenneth D. Forbus
• Odest Chadwicke Jenkins
• Christian Lebiere
• Dario Salvucci
• Matthias Scheutz

This article presents a new research area called interactive task learning (ITL), in which an agent actively tries to learn not just how to perform a task better but the actual definition of a task through natural interaction with a human instructor while attempting to perform the task. The authors provide an analysis of desiderata for ITL systems, a review of related work, and a discussion of possible application areas for ITL systems.

KER Journal 2016 Journal Article

HuroCup: competition for multi-event humanoid robot athletes

• Jacky Baltes
• Kuo-Yang Tu
• Soroush Sadeghnejad
• John Anderson

Abstract This paper describes the motivation for the development of the HuroCup competition and follows the rule development from its inaugural competition from 2002 to 2015. The history of HuroCup is broken down into its growing phase (2002–2006), a time of explosive growth (2007–2011), and current times. This paper describes the main research focus of HuroCup, the multi-event humanoid robot competition: (a) active balancing, (b) complex motion planning, and (c) human–robot interaction and shows how the various HuroCup events relate to those research topics. This paper concludes with some medium- and long-term goals of the rule development for HuroCup.

KER Journal 2011 Journal Article

Robotics competitions as benchmarks for AI research

• John Anderson
• Jacky Baltes
• Chi Tai Cheng

Abstract In the last two decades various intelligent robotics competitions have become very popular. Arguably the most well-known of these are the robotic soccer competitions. In addition to their value in attracting media and capturing the minds of the general public, these competitions also provide benchmark problems for various robotics and artificial intelligence (AI) technologies. As with any benchmark, care must be taken that the benchmark does not introduce unwarranted biases. This paper critically evaluates the AI contributions made by various robotic competitions on AI research.

IS Journal 2011 Journal Article

Using Brain Imaging to Interpret Student Problem Solving

• John Anderson
• Shawn Betts
• Jennifer Ferris
• Jon Fincham
• Jian Yang

We have been exploring whether multi voxel pattern analysis (MVPA) of functional magnet resonance imaging (fMRI) data can be used to infer the mental states of students learning mathematics. This approach has shown considerable success in tracking static mental states such as whether a person is thinking about a location or an animal. Applying this to our case involves significant challenges not faced in many MVPA applications because it is necessary to track changing student states over time. The paths of states that students take in solving problems can be quite variable. Nevertheless, we have achieved relatively high accuracy in determining what step a student is on when solving a sequence of problems and whether that step is being performed correctly. Hidden Markov models can then be used to combine behavioral and brain-imaging data from an intelligent tutoring system to track mental states during student's problem-solving episodes.

AAMAS Conference 2011 Conference Paper

Vision-Based Obstacle Run for Teams of Humanoid Robots

• Jacky Baltes
• Chi Tai Cheng
• Jonathan Bagot
• John Anderson

This demonstration shows a team of small humanoid robots traverse an environment through a set of obstacles. The robots' brain are implemented using mobile phones for vision, balance, and processing. The robots use particle filters to localize themselves and to map the environment. A frontier-based exploration algorithm is used to direct the robots to overcome obstacles and to explore all regions of the environment.

AAAI Conference 2010 Conference Paper

Leveraging Mixed Reality Infrastructure for Robotics and Applied AI Instruction

• Jacky Baltes
• John Anderson

Mixed reality is an important classroom tool for managing complexity from both the students’ and instructor’s standpoints. It can be used to provide important scaffolds when introducing robotics, by allowing elements of perception and control to be abstracted, and these abstractions removed as a course progresses (or left in place to introduce robotics to younger groups of students). In prior work, we have illustrated the potential of this approach both in providing scaffolding, building an inexpensive robotics laboratory, and also providing control of evaluation of robotics environments for student evaluation and scientific experimentation. In this paper, we explore integrating extensions and improvements to the mixed reality components themselves as part of a course in applied artificial intelligence and robotics. We present a set of assignments that in addition to exploring robotics concepts, actively integrate creating or improving mixed reality components. We find that this approach better leverages the advantages brought about by mixed reality in terms of student motivation, and also provides some very useful software engineering experience to the students.

IROS Conference 2007 Conference Paper

A local approach to developing grounded spatial references in multi-robot systems

• Nathan Wiebe
• John Anderson

For a mobile robot to be able to communicate usefully with others, the symbols it uses to communicate must be grounded to entities in the environment, and those groundings made consistent among agents. While it is common practice to hand-construct such groundings, this does not scale to large problems. In particular, when communicating about useful spatial references, there are a large number of potentially relevant groundings, even for a basic task such as navigation. This paper describes the development and evaluation of an approach that allows a group of robotic agents to develop consistent shared groundings for locations in an environment over time. This approach is based on local communication and interaction, and does not rely on the ability to broadcast references to all agents, and so is suitable for domains in which communication may be sporadic, such as robotic rescue. The evaluation of this approach, which compares several different grounding techniques, shows that shared groundings can be developed effectively over time, and that these improve the effectiveness of communication in a multi-robot setting.

AAAI Conference 2007 Conference Paper

A Mixed Reality Approach to Undergraduate Robotics Education

• John Anderson

Teaching robotics to undergraduate students requires a course framework that allows students to learn about robotics in stages, without being overwhelmed with details. Such a framework must also provide the students with a motivating application environment that challenges them to apply what they have learned. Robotics competitions have proven to be an excellent method for motivating students, so the framework should be portable and robust enough to be used for competitions, and flexible enough to provide a range of environments that can become more challenging as students become more adept. Finally, the framework should provide repeatability and control for evaluating the student’s work, as well as for performing research. In this paper, we overview a mixed reality approach that meets these criteria, and describe its use in an advanced undergraduate course.