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Found 34 papers, 15 papers with code
Efficient Model Learning for Human-Robot Collaborative Tasks
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.
Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning
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.
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
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.
Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates
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)?
Zero-Shot Imitating Collaborative Manipulation Plans from YouTube Cooking Videos
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.
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
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.
Fair Contextual Multi-Armed Bandits: Theory and Experiments
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.
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels.
Video Game Level Repair via Mixed Integer Linear Programming
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples.
Learning from Demonstrations using Signal Temporal Logic
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.
Differentiable Quality Diversity
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.
On the Importance of Environments in Human-Robot Coordination
When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks.
Illuminating Diverse Neural Cellular Automata for Level Generation
We present a method of generating diverse collections of neural cellular automata (NCA) to design video game levels.
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding
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.
Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
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.
Learning Performance Graphs from Demonstrations via Task-Based Evaluations
In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots.
Covariance Matrix Adaptation MAP-Annealing
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.
Deep Surrogate Assisted Generation of Environments
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.
Generating Diverse Indoor Furniture Arrangements
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.
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment
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.
Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing
Pre-training a diverse set of neural network controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks.
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research.
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization
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.
Surrogate Assisted Generation of Human-Robot Interaction Scenarios
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.
Multi-Robot Coordination and Layout Design for Automated Warehousing
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.
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning.
BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning
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.
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
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.
Signal Temporal Logic-Guided Apprenticeship Learning
Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations.
Density Descent for Diversity Optimization
We propose Density Descent Search (DDS), an algorithm that explores the feature space via gradient descent on a continuous density estimate of the feature space that also provides stronger stability guarantee.
Quality-Diversity Generative Sampling for Learning with Synthetic Data
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.
Singing the Body Electric: The Impact of Robot Embodiment on User Expectations
As a result, understanding conceptualizations formed from physical design is necessary to understand how users intend to interact with the robot.
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in four benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3000 agents.
Using Causal Trees to Estimate Personalized Task Difficulty in Post-Stroke Individuals
Adaptive training programs are crucial for recovery post stroke.
