Collaborating Authors

Tokyo mecha bar targets middle-aged fans of the anime genre as it reopens following remodeling work

The Japan Times

Sipping booze while being surrounded by a small army of robot figures may not be a relaxing setting for ordinary bar-goers. But that's the sort of place where middle-aged fans of the mecha anime genre, which focuses on giant robots or machines controlled by people, would likely drop by and schmooze about their favorite characters or scenes. After going through some remodeling, Robot Kichi in Tokyo's Ikebukuro district will reopen Thursday and welcome enthusiasts of the genre. The remodeled bar boasts a large, white U-shaped counter that can seat 20 people and showcases about 100 robot figures, including those from "Mazinger Z," "King of Braves GaoGaiGar" and the "Gundam" series. Customers can also watch anime on large screens.

Robot-Building Lab and Contest at the 1993 National AI Conference

AI Magazine

A robot-building lab and contest was held at the Eleventh National Conference on Artificial Intelligence. Teams of three worked day and night for 72 hours to build tabletop autonomous robots of legos, a small microcontroller board, and sensors. The robots then competed head to head in two events. I was one of the developers of JACK, the second-place finisher in the Coffeepot event. This article contains my personal recollections of the lab and contest.

Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity Machine Learning

Transfer learning methods for reinforcement learning (RL) domains facilitate the acquisition of new skills using previously acquired knowledge. The vast majority of existing approaches assume that the agents have the same design, e.g. same shape and action spaces. In this paper we address the problem of transferring previously acquired skills amongst morphologically different agents (MDAs). For instance, assuming that a bipedal agent has been trained to move forward, could this skill be transferred on to a one-leg hopper so as to make its training process for the same task more sample efficient? We frame this problem as one of subspace learning whereby we aim to infer latent factors representing the control mechanism that is common between MDAs. We propose a novel paired variational encoder-decoder model, PVED, that disentangles the control of MDAs into shared and agent-specific factors. The shared factors are then leveraged for skill transfer using RL. Theoretically, we derive a theorem indicating how the performance of PVED depends on the shared factors and agent morphologies. Experimentally, PVED has been extensively validated on four MuJoCo environments. We demonstrate its performance compared to a state-of-the-art approach and several ablation cases, visualize and interpret the hidden factors, and identify avenues for future improvements.

CMUNITED-97: RoboCup-97 Small-Robot World Champion Team

AI Magazine

Robotic soccer is a challenging research domain that involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this article, we describe CMUNITED, the team of small robotic agents that we developed to enter the RoboCup-97 competition. We designed and built the robotic agents, devised the appropriate vision algorithm, and developed and implemented algorithms for strategic collaboration between the robots in an uncertain and dynamic environment. The robots can organize themselves in formations, hold specific roles, and pursue their goals. In game situations, they have demonstrated their collaborative behaviors on multiple occasions. We present an overview of the vision-processing algorithm that successfully tracks multiple moving objects and predicts trajectories. The article then focuses on the agent behaviors, ranging from low-level individual behaviors to coordinated, strategic team behaviors. CMUNITED won the RoboCup-97 small-robot competition at the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan.

RoboCup: The Robot World Cup Initiative

AAAI Conferences

The Robot World Cup Initiative (R, oboCup) is attempt to foster AI and intelligent rohoties research by providing a standard problem where wide range of technologies especially concerning multi-agent research (:an be integrated and examined. The first RoboCup competition is to be, heht at. In order for a robot team to actually perform a soccer game. Unlike AAAI robot competition, which is tuned for a single heavy-duty slow-moving robot. RoboCup is a task for a team of multiple f'ast-moving robots under a dynamic environmen(.