16 citations · 52 across the 20 of their papers we have counts for
20 papers
Mil-SCORE: Benchmarking Long-Context Geospatial Reasoning and Planning in Large Language Models
Aadi Palnitkar, Mingyang Mao, Nicholas Waytowich +2
As large language models (LLMs) are applied to increasingly longer and more complex tasks, there is a growing need for realistic long-context benchmarks that require selective read…
"New" Challenges for Future C2: Commanding Soldier-Machine Partnerships
Anna Madison, Kaleb McDowell, Vinicius G. Goecks +7
Future warfare will occur in more complex, fast-paced, ill-structured, and demanding conditions that will stress current Command and Control (C2) systems. Without modernization, th…
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games
Nicholas R. Waytowich, Devin White, MD Sunbeam +1
Recent advancements in large language models (LLMs) have expanded their capabilities beyond traditional text-based tasks to multimodal domains, integrating visual, auditory, and te…
Re-Envisioning Command and Control
Kaleb McDowell, Ellen Novoseller, Anna Madison +2
Future warfare will require Command and Control (C2) decision-making to occur in more complex, fast-paced, ill-structured, and demanding conditions. C2 will be further complicated…
Scalable Interactive Machine Learning for Future Command and Control
Anna Madison, Ellen Novoseller, Vinicius G. Goecks +7
Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations. Given the need for robus…
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
Vinicius G. Goecks, Nicholas Waytowich
The development of Courses of Action (COAs) in military operations is traditionally a time-consuming and intricate process. Addressing this challenge, this study introduces COA-GPT…
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft
Ellen Novoseller, Vinicius G. Goecks, David Watkins +2
In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in…
DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios
Vinicius G. Goecks, Nicholas R. Waytowich
The development of plans of action in disaster response scenarios is a time-consuming process. Large Language Models (LLMs) offer a powerful solution to expedite this process throu…
Learning Flight Control Systems from Human Demonstrations and Real-Time Uncertainty-Informed Interventions
Prashant Ganesh, J. Humberto Ramos, Vinicius G. Goecks +4
This paper describes a methodology for learning flight control systems from human demonstrations and interventions while considering the estimated uncertainty in the learned models…
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas +27
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 20…
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II
Nicholas Waytowich, James Hare, Vinicius G. Goecks +4
Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-qua…
Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft
Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls +1
Real-world tasks of interest are generally poorly defined by human-readable descriptions and have no pre-defined reward signals unless it is defined by a human designer. Conversely…
On games and simulators as a platform for development of artificial intelligence for command and control
Vinicius G. Goecks, Nicholas Waytowich, Derrik E. Asher +9
Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: mu…
Imitation Learning with Human Eye Gaze via Multi-Objective Prediction
Ravi Kumar Thakur, MD-Nazmus Samin Sunbeam, Vinicius G. Goecks +6
Approaches for teaching learning agents via human demonstrations have been widely studied and successfully applied to multiple domains. However, the majority of imitation learning…
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems
Vinicius G. Goecks
Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. T…
Combining Visible and Infrared Spectrum Imagery using Machine Learning for Small Unmanned Aerial System Detection
Vinicius G. Goecks, Grayson Woods, John Valasek
Advances in machine learning and deep neural networks for object detection, coupled with lower cost and power requirements of cameras, led to promising vision-based solutions for s…
PODNet: A Neural Network for Discovery of Plannable Options
Ritwik Bera, Vinicius G. Goecks, Gregory M. Gremillion +2
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives.…
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern +2
This paper investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. It is well-k…
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern +2
This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and intervent…
Cycle-of-Learning for Autonomous Systems from Human Interaction
Nicholas R. Waytowich, Vinicius G. Goecks, Vernon J. Lawhern
We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms. We provide a taxonomy to categorize the typ…