Publications (24)
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…
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…
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…
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…
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…
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…