36 citations · 47 across the 23 of their papers we have counts for
26 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…
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…
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…
Materials Matter: Investigating Functional Advantages of Bio-Inspired Materials via Simulated Robotic Hopping
Andrew K. Schulz, Ayah G. Ahmad, Maegan Tucker
In contrast with the diversity of materials found in nature, most robots are designed with some combination of aluminum, stainless steel, and 3D-printed filament. Additionally, rob…
Preferential Multi-Objective Bayesian Optimization
Raul Astudillo, Kejun Li, Maegan Tucker +3
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's latent preferences over available design choices. While preferences often involve multiple…
Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion
Maegan Tucker, Kejun Li, Aaron D. Ames
Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work…
Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics
Adrian B. Ghansah, Jeeseop Kim, Maegan Tucker +1
Selecting robot design parameters can be challenging since these parameters are often coupled with the performance of the controller and, therefore, the resulting capabilities of t…
Input-to-State Stability in Probability
Preston Culbertson, Ryan K. Cosner, Maegan Tucker +1
Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances. If a system is ISS, its trajectories wil…
An Input-to-State Stability Perspective on Robust Locomotion
Maegan Tucker, Aaron D. Ames
Uneven terrain necessarily transforms periodic walking into a non-periodic motion. As such, traditional stability analysis tools no longer adequately capture the ability of a biped…
Robust Bipedal Locomotion: Leveraging Saltation Matrices for Gait Optimization
Maegan Tucker, Noel Csomay-Shanklin, Aaron D. Ames
The ability to generate robust walking gaits on bipedal robots is key to their successful realization on hardware. To this end, this work extends the method of Hybrid Zero Dynamics…
Safety-Aware Preference-Based Learning for Safety-Critical Control
Ryan K. Cosner, Maegan Tucker, Andrew J. Taylor +7
Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conser…
Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics
Kejun Li, Maegan Tucker, Rachel Gehlhar +2
Generating stable walking gaits that yield natural locomotion when executed on robotic-assistive devices is a challenging task that often requires hand-tuning by domain experts. Th…
Learning Controller Gains on Bipedal Walking Robots via User Preferences
Noel Csomay-Shanklin, Maegan Tucker, Min Dai +2
Experimental demonstration of complex robotic behaviors relies heavily on finding the correct controller gains. This painstaking process is often completed by a domain expert, requ…
Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots
Maegan Tucker, Noel Csomay-Shanklin, Wen-Loong Ma +1
This paper presents a framework that leverages both control theory and machine learning to obtain stable and robust bipedal locomotion without the need for manual parameter tuning.…
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…
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
Maegan Tucker, Myra Cheng, Ellen Novoseller +4
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space. However, existing preferenc…
Stabilization of Exoskeletons through Active Ankle Compensation
Thomas Gurriet, Maegan Tucker, Claudia Kann +2
This paper presents an active stabilization method for a fully actuated lower-limb exoskeleton. The method was tested on the exoskeleton ATALANTE, which was designed and built by t…