20 citations · 29 across the 17 of their papers we have counts for
19 papers
Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control
Albert H. Li, Brandon Hung, Aaron D. Ames +3
Recent advancements in parallel simulation and successful robotic applications are spurring a resurgence in sampling-based model predictive control. To build on this progress, howe…
SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
Lizhi Yang, Blake Werner, Ryan K. Cosner +3
Robot learning has produced remarkably effective ``black-box'' controllers for complex tasks such as dynamic locomotion on humanoids. Yet ensuring dynamic safety, i.e., constraint…
Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer
Tyler Ga Wei Lum, Albert H. Li, Preston Culbertson +4
This work explores conditions under which multi-finger grasping algorithms can attain robust sim-to-real transfer. While numerous large datasets facilitate learning generative mode…
DROP: Dexterous Reorientation via Online Planning
Albert H. Li, Preston Culbertson, Vince Kurtz +1
Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (R…
Toward An Analytic Theory of Intrinsic Robustness for Dexterous Grasping
Albert H. Li, Preston Culbertson, Aaron D. Ames
Conventional approaches to grasp planning require perfect knowledge of an object's pose and geometry. Uncertainties in these quantities induce uncertainties in the quality of plann…
Bounding Stochastic Safety: Leveraging Freedman's Inequality with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Preston Culbertson, Aaron D. Ames
When deployed in the real world, safe control methods must be robust to unstructured uncertainties such as modeling error and external disturbances. Typical robust safety methods a…
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Igor Sadalski, Jana K. Woo +2
A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances. Most existing approaches to robust control either seek to bound the…
PONG: Probabilistic Object Normals for Grasping via Analytic Bounds on Force Closure Probability
Albert H. Li, Preston Culbertson, Aaron D. Ames
Classical approaches to grasp planning are deterministic, requiring perfect knowledge of an object's pose and geometry. In response, data-driven approaches have emerged that plan g…
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…
Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Preston Culbertson, Andrew J. Taylor +1
Robots deployed in unstructured, real-world environments operate under considerable uncertainty due to imperfect state estimates, model error, and disturbances. Given this real-wor…
FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Metric
Albert H. Li, Preston Culbertson, Joel W. Burdick +1
Many approaches to grasp synthesis optimize analytic quality metrics that measure grasp robustness based on finger placements and local surface geometry. However, generating feasib…
CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes
Timothy Chen, Preston Culbertson, Mac Schwager
We introduce a transformation of a Neural Radiance Field (NeRF) to an equivalent Poisson Point Process (PPP). This PPP transformation allows for rigorous quantification of uncertai…
Vision-Only Robot Navigation in a Neural Radiance World
Michal Adamkiewicz, Timothy Chen, Adam Caccavale +4
Neural Radiance Fields (NeRFs) have recently emerged as a powerful paradigm for the representation of natural, complex 3D scenes. NeRFs represent continuous volumetric density and…
TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation
Claire Chen, Preston Culbertson, Marion Lepert +2
Dexterous manipulation tasks often require contact switching, where fingers make and break contact with the object. We propose a method that plans trajectories for dexterous manipu…
CoCo: Online Mixed-Integer Control via Supervised Learning
A. Cauligi, P. Culbertson, E. Schmerling +3
Many robotics problems, from robot motion planning to object manipulation, can be modeled as mixed-integer convex programs (MICPs). However, state-of-the-art algorithms are still u…
Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies
Preston Culbertson, Jean-Jacques E. Slotine, Mac Schwager
In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in . The robots do not know the mass or friction prop…
Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control
A. Cauligi, P. Culbertson, B. Stellato +3
Mixed-integer convex programming (MICP) has seen significant algorithmic and hardware improvements with several orders of magnitude solve time speedups compared to 25 years ago. De…
Estimation and Control Using Sampling-Based Bayesian Reinforcement Learning
Patrick Slade, Zachary N. Sunberg, Mykel J. Kochenderfer
Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to m…
Simultaneous active parameter estimation and control using sampling-based Bayesian reinforcement learning
Patrick Slade, Preston Culbertson, Zachary Sunberg +1
Robots performing manipulation tasks must operate under uncertainty about both their pose and the dynamics of the system. In order to remain robust to modeling error and shifts in…