5 papers
Multi-Objective Human-in-the-Loop Bayesian Optimization of a Lower-Limb Exoskeleton
Neil Janwani, Matthew T. Lerner, Aaron J. Young +1
Human-in-the-loop optimization (HILO) is a common approach for optimizing the control of assistive devices to account for the wearer's unique biomechanics and subjective preference…
MOCHA: Multi-Objective Co-Design using Hypernetwork Architectures
Varun Madabushi, Neil Janwani, Maegan Tucker
In this work, we present MOCHA, the first, to our knowledge, reinforcement learning based approach to computing a family of Pareto-optimal policies across the design space of a rob…
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…
NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Neil Janwani, Varun Madabushi, Maegan Tucker
Reinforcement learning (RL) has emerged as a powerful method to learn robust control policies for bipedal locomotion. Yet, it can be difficult to tune desired robot behaviors due t…
A Learning-Based Framework for Safe Human-Robot Collaboration with Multiple Backup Control Barrier Functions
Neil C. Janwani, Ersin Daş, Thomas Touma +3
Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that lever…