16 papers
Generate, Track, Improve: Perceptive Multi-Skill Humanoid Locomotion with RL-Fine-Tuned Motion Generators
Zachary Olkin, William D. Compton, Aaron D. Ames
General purpose humanoids require locomotion controllers that are multi-skill, perceptive, dynamic, and robust enough to go anywhere humans can. In this work, we present a two laye…
Learning Safe Humanoid Navigation from Reduced Order Models
William D. Compton, Zachary Olkin, Ryan Bena +1
Research in humanoid robotics has achieved rapid progress in locomotion, and recent results have pushed the boundary on autonomous navigation. We demonstrate that a standard single…
Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
William D. Compton, Zachary Olkin, Aaron D. Ames
We present a method for training reference-guided, perceptive reinforcement learning locomotion policies for humanoid robots in which reference trajectories are modulated in traini…
Stability of Control Lyapunov Function Guided Reinforcement Learning
Zachary Olkin, William D. Compton, Aaron D. Ames
Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corresponding control policies is…
Decoding Defensive Coverage Responsibilities in American Football Using Factorized Attention Based Transformer Models
Kevin Song, Evan Diewald, Ornob Siddiquee +4
Defensive coverage schemes in the National Football League (NFL) represent complex tactical patterns requiring coordinated assignments among defenders who must react dynamically to…
Compatibility of Multiple Control Barrier Functions for Constrained Nonlinear Systems
Max H. Cohen, Eugene Lavretsky, Aaron D. Ames
Control barrier functions (CBFs) are a powerful tool for the constrained control of nonlinear systems; however, the majority of results in the literature focus on systems subject t…
CLF-RL: Control Lyapunov Function Guided Reinforcement Learning
Kejun Li, Zachary Olkin, Yisong Yue +1
Reinforcement learning (RL) has shown promise in generating robust locomotion policies for bipedal robots, but often suffers from tedious reward design and sensitivity to poorly sh…
Layered Nonlinear Model Predictive Control for Robust Stabilization of Hybrid Systems
Zachary Olkin, Aaron D. Ames
Computing the receding horizon optimal control of nonlinear hybrid systems is typically prohibitively slow, limiting real-time implementation. To address this challenge, we propose…
Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions
William D. Compton, Max H. Cohen, Aaron D. Ames
Safety filters leveraging control barrier functions (CBFs) are highly effective for enforcing safe behavior on complex systems. It is often easier to synthesize CBFs for a Reduced…
Safety-Critical Controller Synthesis with Reduced-Order Models
Max H. Cohen, Noel Csomay-Shanklin, William D. Compton +2
Reduced-order models (ROMs) provide lower dimensional representations of complex systems, capturing their salient features while simplifying control design. Building on previous wo…
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…
Bilevel Optimization for Real-Time Control with Application to Locomotion Gait Generation
Zachary Olkin, Aaron D. Ames
Model Predictive Control (MPC) is a common tool for the control of nonlinear, real-world systems, such as legged robots. However, solving MPC quickly enough to enable its use in re…
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…
Constructive Nonlinear Control of Underactuated Systems via Zero Dynamics Policies
William Compton, Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin +2
Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics. Decomposing the…