Search Results for author:
Found 39 papers, 18 papers with code
Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation
For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly.
Multi-Camera Asynchronous Ball Localization and Trajectory Prediction with Factor Graphs and Human Poses
The rapid and precise localization and prediction of a ball are critical for developing agile robots in ball sports, particularly in sports like tennis characterized by high-speed ball movements and powerful spins.
Diffusion Models for Multi-target Adversarial Tracking
Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limited.
Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation
We propose a deep learning-based framework, called HybridNet, combining a heterogeneous graph-based encoder with a recurrent schedule propagator for scheduling stochastic human-robot teams under upper- and lower-bound temporal constraints.
Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks
While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems.
Towards Reconciling Usability and Usefulness of Explainable AI Methodologies
Our findings highlight internal consistency issues wherein participants perceived language explanations to be significantly more usable, however participants were better able to objectively understand the decision making process of the car through a decision tree explanation.
Decision Making
Explainable Artificial Intelligence (XAI)
+1
Safe Inverse Reinforcement Learning via Control Barrier Function
Learning from Demonstration (LfD) is a powerful method for enabling robots to perform novel tasks as it is often more tractable for a non-roboticist end-user to demonstrate the desired skill and for the robot to efficiently learn from the associated data than for a human to engineer a reward function for the robot to learn the skill via reinforcement learning (RL).
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings
Federated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server.
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
On the other hand, expert performance degrades with the addition of xAI-based support ($p<0. 05$), indicating that the cost of paying attention to the xAI outweighs the benefits obtained from being provided additional information to enhance SA.
A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting
Leveraging a game environment, we collect a dataset of over 1000 examples, mapping language strategies to the corresponding goals and constraints, and show that our model, trained on this dataset, significantly outperforms human interpreters in inferring strategic intent (i. e., goals and constraints) from language (p < 0. 05).
Robots Enact Malignant Stereotypes
Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14].
Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees
Our approach enables UAVs to infer the latent fire propagation dynamics for time-extended coordination in safety-critical conditions.
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees
In each test, CA-RRT reached more states on average in the same number of iterations as weighted-RRT.
Strategy Discovery and Mixture in Lifelong Learning from Heterogeneous Demonstration
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.
Learning Interpretable, High-Performing Policies for Autonomous Driving
While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD).
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming
Information sharing is key in building team cognition and enables coordination and cooperation.
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer
Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-specific training.
Guiding Multi-Step Rearrangement Tasks with Natural Language Instructions
Our model completes block manipulation tasks with synthetic commands 530 more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning.
Learning to Follow Language Instructions with Compositional Policies
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions.
Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform novel tasks by providing demonstrations.
Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers
The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users.
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy.
A Generalized Robotic Handwriting Learning System based on Dynamic Movement Primitives (DMPs)
Learning from demonstration (LfD) is a powerful learning method to enable a robot to infer how to perform a task given one or more human demonstrations of the desired task.
Robotics
Cross-Loss Influence Functions to Explain Deep Network Representations
As machine learning is increasingly deployed in the real world, it is paramount that we develop the tools necessary to analyze the decision-making of the models we train and deploy to end-users.
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot Teams
The FireCommander environment can be useful for research topics spanning a wide range of applications from Reinforcement Learning (RL) and Learning from Demonstration (LfD), to Coordination, Psychology, Human-Robot Interaction (HRI) and Teaming.
Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration.
Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies: Scheduling with Learning Curves
As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive.
Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires
Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path.
Heterogeneous Learning from Demonstration
This inference requires the robot to be able to detect and classify the heterogeneity of its partners.
Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation
On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations.
When Your Robot Breaks: Active Learning During Plant Failure
Detecting and adapting to catastrophic failures in robotic systems requires a robot to learn its new dynamics quickly and safely to best accomplish its goals.
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks
Increasing interest in integrating advanced robotics within manufacturing has spurred a renewed concentration in developing real-time scheduling solutions to coordinate human-robot collaboration in this environment.
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints.
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Decision trees are ubiquitous in machine learning for their ease of use and interpretability.
Safe Coordination of Human-Robot Firefighting Teams
Wildfires are destructive and inflict massive, irreversible harm to victims' lives and natural resources.
Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
For assistive robots and virtual agents to achieve ubiquity, machines will need to anticipate the needs of their human counterparts.
Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning
Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation.
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem.
