Search Results for author: Stefanos Nikolaidis

Found 34 papers, 16 papers with code

Guidance Graph Optimization for Lifelong Multi-Agent Path Finding

2 code implementations2 Feb 2024 Yulun Zhang, He Jiang, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li

In this work, we introduce the guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization as the task of optimizing its edge weights.

Multi-Agent Path Finding

Singing the Body Electric: The Impact of Robot Embodiment on User Expectations

no code implementations13 Jan 2024 Nathaniel Dennler, Stefanos Nikolaidis, Maja Matarić

As a result, understanding conceptualizations formed from physical design is necessary to understand how users intend to interact with the robot.

Quality-Diversity Generative Sampling for Learning with Synthetic Data

1 code implementation22 Dec 2023 Allen Chang, Matthew C. Fontaine, Serena Booth, Maja J. Matarić, Stefanos Nikolaidis

QDGS is a model-agnostic framework that uses prompt guidance to optimize a quality objective across measures of diversity for synthetically generated data, without fine-tuning the generative model.


Density Descent for Diversity Optimization

no code implementations18 Dec 2023 David H. Lee, Anishalakshmi V. Palaparthi, Matthew C. Fontaine, Bryon Tjanaka, Stefanos Nikolaidis

We propose Density Descent Search (DDS), an algorithm that explores the feature space via CMA-ES on a continuous density estimate of the feature space that also provides a stronger stability guarantee.

Density Estimation

Signal Temporal Logic-Guided Apprenticeship Learning

no code implementations9 Nov 2023 Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis

Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations.

Arbitrarily Scalable Environment Generators via Neural Cellular Automata

1 code implementation NeurIPS 2023 Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li

We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2, 350 robots.

BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning

no code implementations16 Oct 2023 Tianle Huang, Nitish Sontakke, K. Niranjan Kumar, Irfan Essa, Stefanos Nikolaidis, Dennis W. Hong, Sehoon Ha

Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world.

Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning

no code implementations23 May 2023 Sumeet Batra, Bryon Tjanaka, Matthew C. Fontaine, Aleksei Petrenko, Stefanos Nikolaidis, Gaurav Sukhatme

Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning.

reinforcement-learning Reinforcement Learning (RL)

Multi-Robot Coordination and Layout Design for Automated Warehousing

1 code implementation10 May 2023 Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li

We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability.

Layout Design Multi-Agent Path Finding

Surrogate Assisted Generation of Human-Robot Interaction Scenarios

1 code implementation26 Apr 2023 Varun Bhatt, Heramb Nemlekar, Matthew C. Fontaine, Bryon Tjanaka, Hejia Zhang, Ya-Chuan Hsu, Stefanos Nikolaidis

In the shared control teleoperation domain and a more complex shared workspace collaboration task, we show that surrogate assisted scenario generation efficiently synthesizes diverse datasets of challenging scenarios.

pyribs: A Bare-Bones Python Library for Quality Diversity Optimization

1 code implementation1 Mar 2023 Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Yulun Zhang, Nivedit Reddy Balam, Nathaniel Dennler, Sujay S. Garlanka, Nikitas Dimitri Klapsis, Stefanos Nikolaidis

Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem.

PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection

no code implementations9 Dec 2022 Shivin Dass, Karl Pertsch, Hejia Zhang, Youngwoon Lee, Joseph J. Lim, Stefanos Nikolaidis

Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research.

Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing

1 code implementation6 Oct 2022 Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Aniruddha Kalkar, Stefanos Nikolaidis

Pre-training a diverse set of neural network controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks.

Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment

no code implementations4 Aug 2022 Yilei Zeng, Jiali Duan, Yang Li, Emilio Ferrara, Lerrel Pinto, C. -C. Jay Kuo, Stefanos Nikolaidis

In this work, we guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process.

reinforcement-learning Reinforcement Learning (RL)

Generating Diverse Indoor Furniture Arrangements

no code implementations20 Jun 2022 Ya-Chuan Hsu, Matthew C. Fontaine, Sam Earle, Maria Edwards, Julian Togelius, Stefanos Nikolaidis

To target specific diversity in the arrangements, we optimize the latent space of the GAN via a quality diversity algorithm to generate a diverse arrangement collection.

Generative Adversarial Network

Deep Surrogate Assisted Generation of Environments

no code implementations9 Jun 2022 Varun Bhatt, Bryon Tjanaka, Matthew C. Fontaine, Stefanos Nikolaidis

Results in two benchmark domains show that DSAGE significantly outperforms existing QD environment generation algorithms in discovering collections of environments that elicit diverse behaviors of a state-of-the-art RL agent and a planning agent.

Reinforcement Learning (RL)

Covariance Matrix Adaptation MAP-Annealing

1 code implementation22 May 2022 Matthew C. Fontaine, Stefanos Nikolaidis

Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of solutions that are both high-quality with respect to an objective and diverse with respect to specified measure functions.

Learning Performance Graphs from Demonstrations via Task-Based Evaluations

no code implementations12 Apr 2022 Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis

In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots.

Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning

1 code implementation8 Feb 2022 Bryon Tjanaka, Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis

Training can then be viewed as a quality diversity (QD) optimization problem, where we search for a collection of performant policies that are diverse with respect to quantified behavior.

reinforcement-learning Reinforcement Learning (RL)

Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding

1 code implementation7 Dec 2021 Yulun Zhang, Matthew C. Fontaine, Amy K. Hoover, Stefanos Nikolaidis

In a Hearthstone deckbuilding case study, we show that our approach improves the sample efficiency of MAP-Elites and outperforms a model trained offline with random decks, as well as a linear surrogate model baseline, setting a new state-of-the-art for quality diversity approaches in automated Hearthstone deckbuilding.

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 diverse collections of neural cellular automata (NCA) to design video game levels.

On the Importance of Environments in Human-Robot Coordination

1 code implementation21 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

2 code implementations 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 reinforcement-learning +1

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.

Generative Adversarial Network

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

6 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.

Zero-Shot Imitating Collaborative Manipulation Plans from YouTube Cooking Videos

no code implementations25 Nov 2019 Hejia Zhang, Jie Zhong, Stefanos Nikolaidis

Building upon this theory of language for action, we propose a system for understanding and executing demonstrated action sequences from full-length, real-world cooking videos on the web.

Action Detection

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.


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