Search Results for author: Stefanos Nikolaidis

Found 13 papers, 6 papers with code

Illuminating Diverse Neural Cellular Automata for Level Generation

2 code implementations12 Sep 2021 Sam Earle, Justin Snider, Matthew C. Fontaine, Stefanos Nikolaidis, Julian Togelius

We present a method of generating a collection of neural cellular automata (NCA) to design video game levels.

On the Importance of Environments in Human-Robot Coordination

3 code implementations21 Jun 2021 Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang, Bryon Tjanaka, Stefanos Nikolaidis

When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks.

Differentiable Quality Diversity

1 code implementation NeurIPS 2021 Matthew C. Fontaine, Stefanos Nikolaidis

Quality diversity (QD) is a growing branch of stochastic optimization research that studies the problem of generating an archive of solutions that maximize a given objective function but are also diverse with respect to a set of specified measure functions.

Stochastic Optimization

Learning from Demonstrations using Signal Temporal Logic

no code implementations15 Feb 2021 Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis

Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions.

OpenAI Gym Temporal Logic

Video Game Level Repair via Mixed Integer Linear Programming

1 code implementation13 Oct 2020 Hejia Zhang, Matthew C. Fontaine, Amy K. Hoover, Julian Togelius, Bistra Dilkina, Stefanos Nikolaidis

Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples.

Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network

1 code implementation11 Jul 2020 Matthew C. Fontaine, Ruilin Liu, Ahmed Khalifa, Jignesh Modi, Julian Togelius, Amy K. Hoover, Stefanos Nikolaidis

Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels.

Fair Contextual Multi-Armed Bandits: Theory and Experiments

no code implementations13 Dec 2019 Yifang Chen, Alex Cuellar, Haipeng Luo, Jignesh Modi, Heramb Nemlekar, Stefanos Nikolaidis

We introduce a Multi-Armed Bandit algorithm with fairness constraints, where fairness is defined as a minimum rate that a task or a resource is assigned to a user.

Decision Making Fairness +1

Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space

3 code implementations5 Dec 2019 Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis, Amy K. Hoover

Results from experiments based on standard continuous optimization benchmarks show that CMA-ME finds better-quality solutions than MAP-Elites; similarly, results on the strategic game Hearthstone show that CMA-ME finds both a higher overall quality and broader diversity of strategies than both CMA-ES and MAP-Elites.

Robot Learning and Execution of Collaborative Manipulation Plans from YouTube Cooking Videos

no code implementations25 Nov 2019 Hejia Zhang, Stefanos Nikolaidis

On the other hand, previous work has shown that the space of human manipulation actions has a linguistic, hierarchical structure that relates actions to manipulated objects and tools.

Hierarchical structure

Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates

no code implementations30 Jun 2019 Houston Claure, Yifang Chen, Jignesh Modi, Malte Jung, Stefanos Nikolaidis

How should a robot that collaborates with multiple people decide upon the distribution of resources (e. g. social attention, or parts needed for an assembly)?

Fairness Multi-Armed Bandits

Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills

no code implementations29 Sep 2018 Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme

Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration.

Motion Planning

Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning

no code implementations12 Jan 2018 Min Chen, Stefanos Nikolaidis, Harold Soh, David Hsu, Siddhartha Srinivasa

The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term.

Decision Making

Efficient Model Learning for Human-Robot Collaborative Tasks

no code implementations24 May 2014 Stefanos Nikolaidis, Keren Gu, Ramya Ramakrishnan, Julie Shah

We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human.

Cannot find the paper you are looking for? You can Submit a new open access paper.