19 citations · 38 across the 24 of their papers we have counts for
27 papers
Steerable Pluralistic Policy Alignment from Anonymized Crowd Feedback
Akansha Kalra, Ellen R. Novoseller, Vernon J. Lawhern +1
Pluralistic alignment requires AI systems to learn from diverse populations whose preferences may reflect heterogeneous or even conflicting objectives, and to personalize their beh…
Failure-Resilient Multi-Robot Coordination with -Connectivity Constrained Submodular Maximization
Yoonjae Hwang, Hojune Kim, Ellen Novoseller +2
Multi-robot coordination is essential in applications such as coverage, target tracking, and information gathering, where robots must jointly select actions to maximize team perfor…
MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics
Neil Janwani, Ellen Novoseller, Vernon J. Lawhern +1
Multi-objective reinforcement learning (MORL) is a powerful tool to learn Pareto-optimal policy families across conflicting objectives. However, unlike traditional RL algorithms, e…
GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning
Zhiheng Jiang, Yunzhe Wang, Ryan Marr +3
Preference-Conditioned Policy Learning (PCPL) in Multi-Objective Reinforcement Learning (MORL) approximates diverse Pareto-optimal solutions by conditioning a single policy on user…
R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations
Connor Mattson, Varun Raveendra, Ellen Novoseller +3
Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain. While IL has been widely applied to single-ro…
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm
Chak Lam Shek, Amrit Singh Bedi, Anjon Basak +5
In this work, we present a novel cooperative multi-agent reinforcement learning method called \textbf{Loc}ality based \textbf{Fac}torized \textbf{M}ulti-Agent \textbf{A}ctor-\textb…
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…
Human-in-the-Loop Multi-Robot Information Gathering with Inverse Submodular Maximization
Guangyao Shi, Shipeng Liu, Ellen Novoseller +2
We consider a new type of inverse combinatorial optimization, Inverse Submodular Maximization (ISM), for its application in human-in-the-loop multi-robot information gathering. For…
Crowd-PrefRL: Preference-Based Reward Learning from Crowds
David Chhan, Ellen Novoseller, Vernon J. Lawhern
Preference-based reinforcement learning (RL) provides a framework to train AI agents using human feedback through preferences over pairs of behaviors, enabling agents to learn desi…
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…
Rating-based Reinforcement Learning
Devin White, Mingkang Wu, Ellen Novoseller +3
This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing pre…
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…
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models
Yi Liu, Gaurav Datta, Ellen Novoseller +1
Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted reward function. Ho…
Autonomously Untangling Long Cables
Vainavi Viswanath, Kaushik Shivakumar, Justin Kerr +7
Cables are ubiquitous in many settings and it is often useful to untangle them. However, cables are prone to self-occlusions and knots, making them difficult to perceive and manipu…
Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies
Satvik Sharma, Ellen Novoseller, Vainavi Viswanath +6
Simulation-to-reality transfer has emerged as a popular and highly successful method to train robotic control policies for a wide variety of tasks. However, it is often challenging…
Efficiently Learning Single-Arm Fling Motions to Smooth Garments
Lawrence Yunliang Chen, Huang Huang, Ellen Novoseller +6
Recent work has shown that 2-arm "fling" motions can be effective for garment smoothing. We consider single-arm fling motions. Unlike 2-arm fling motions, which require little robo…
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models
Vincent Lim, Ellen Novoseller, Jeffrey Ichnowski +2
For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensi…
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
Ryan Hoque, Ashwin Balakrishna, Ellen Novoseller +3
Effective robot learning often requires online human feedback and interventions that can cost significant human time, giving rise to the central challenge in interactive imitation…
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan +7
Robot manipulation for untangling 1D deformable structures such as ropes, cables, and wires is challenging due to their infinite dimensional configuration space, complex dynamics,…
Disentangling Dense Multi-Cable Knots
Vainavi Viswanath, Jennifer Grannen, Priya Sundaresan +7
Disentangling two or more cables requires many steps to remove crossings between and within cables. We formalize the problem of disentangling multiple cables and present an algorit…
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning
Ryan Hoque, Ashwin Balakrishna, Carl Putterman +6
Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired be…
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…
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
Kejun Li, Maegan Tucker, Erdem Bıyık +6
Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user's utility landscape. Le…