7 citations · 22 across the 18 of their papers we have counts for
12 papers · 1 filter
Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice
William D. Compton, Noel Csomay-Shanklin, Cole Johnson +1
Safe navigation of cluttered environments is a critical challenge in robotics. It is typically approached by separating the planning and tracking problems, with planning executed o…
Dynamically Feasible Path Planning in Cluttered Environments via Reachable Bezier Polytopes
Noel Csomay-Shanklin, William D. Compton, Aaron D. Ames
The deployment of robotic systems in real world environments requires the ability to quickly produce paths through cluttered, non-convex spaces. These planned trajectories must be…
Bezier Reachable Polytopes: Efficient Certificates for Robust Motion Planning with Layered Architectures
Noel Csomay-Shanklin, Aaron D. Ames
Control architectures are often implemented in a layered fashion, combining independently designed blocks to achieve complex tasks. Providing guarantees for such hierarchical frame…
Robust Agility via Learned Zero Dynamics Policies
Noel Csomay-Shanklin, William D. Compton, Ivan Dario Jimenez Rodriguez +3
We study the design of robust and agile controllers for hybrid underactuated systems. Our approach breaks down the task of creating a stabilizing controller into: 1) learning a map…
Nonlinear Model Predictive Control of a 3D Hopping Robot: Leveraging Lie Group Integrators for Dynamically Stable Behaviors
Noel Csomay-Shanklin, Victor D. Dorobantu, Aaron D. Ames
Achieving stable hopping has been a hallmark challenge in the field of dynamic legged locomotion. Controlled hopping is notably difficult due to extended periods of underactuation…
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…
Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin, Yisong Yue +1
This work presents Neural Gaits, a method for learning dynamic walking gaits through the enforcement of set invariance that can be refined episodically using experimental data from…
Planar Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics
Manuel Y. Galliker, Noel Csomay-Shanklin, Ruben Grandia +4
The ability to generate dynamic walking in real-time for bipedal robots with input constraints and underactuation has the potential to enable locomotion in dynamic, complex and uns…
Verifying Safe Transitions between Dynamic Motion Primitives on Legged Robots
Wyatt Ubellacker, Noel Csomay-Shanklin, Tamas G. Molnar +1
Functional autonomous systems often realize complex tasks by utilizing state machines comprised of discrete primitive behaviors and transitions between these behaviors. This archit…
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai +2
This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only vali…
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.…