36 citations · 47 across the 23 of their papers we have counts for
9 papers · 1 filter
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…
Beyond Kinematics: Benchmarking Simulation Fidelity for Muscle-Driven Imitation Learning
Ayah G. Ahmad, Claire E. Borden, Maegan Tucker
In this work, we conduct a systematic comparison of two state-of-the-art motion-imitation reinforcement learning (MIRL) pipelines, one built on SCONE/HyFyDy and one built on MuJoCo…
Comparison of Non-Deterministic Nonlinear Systems
Shivam Bajaj, Varun S. Madabushi, Maegan Tucker +1
We characterize a notion of system comparison, termed as -similarity, for non-deterministic nonlinear systems. Building on a similar notion recently proposed for stable…
Active Query Synthesis for Preference Learning
Namrata Nadagouda, Nauman Ahad, Maegan Tucker +1
Efficient learning of user preferences is crucial for many modern decision making systems but typically requires costly labeled data. Active learning reduces this cost, yet standar…
Differentiable Invariant Sets for Hybrid Limit Cycles with Application to Legged Robots
Varun Madabushi, Akash Harapanahalli, Samuel Coogan +1
For hybrid systems exhibiting periodic behavior, analyzing the invariant set containing the limit cycle is a natural way to study the robustness of the closed-loop system. However,…
Finite-Step Invariant Sets for Hybrid Systems with Probabilistic Guarantees
Varun Madabushi, Elizabeth Dietrich, Hanna Krasowski +1
Poincare return maps are a fundamental tool for analyzing periodic orbits in hybrid dynamical systems, including legged locomotion, power electronics, and other cyber-physical syst…
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…
Kinodynamic Motion Retargeting for Humanoid Locomotion via Multi-Contact Whole-Body Trajectory Optimization
Xiaoyu Zhang, Steven Haener, Varun Madabushi +1
We present the KinoDynamic Motion Retargeting (KDMR) framework, a novel approach for humanoid locomotion that models the retargeting process as a multi-contact, whole-body trajecto…