4 citations · 16 across the 29 of their papers we have counts for
19 papers · 1 filter
Do My Samples Help? Compute Budget Allocation for Real-World Sampling-Based MPC in Contact-Rich Manipulation
Magnus Dierking, João Carvalho, Vince Kurtz +3
Sampling-based model predictive control (SMPC) is increasingly practical for contact-rich manipulation because GPU-based physics simulators enable real-time evaluation of large num…
Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting
Sergio A. Esteban, Jason H. K. Siu, Derrick Mach +4
Motion retargeting approaches often prioritize kinematic similarity over whole-body dynamics, contact consistency, and actuation limits, yielding references that are difficult for…
On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control
Sergio A. Esteban, Junheng Li, Vince Kurtz +1
Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact-rich sampling-based predictive control (SP…
CENIC: Convex Error-controlled Numerical Integration for Contact
Vince Kurtz, Alejandro Castro
State-of-the-art robotics simulators operate in discrete time. This requires users to choose a time step, which is both critical and challenging: large steps can produce non-physic…
Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option
Rohan Thakker, Adarsh Patnaik, Vince Kurtz +10
Safe, reliable navigation in extreme, unfamiliar terrain is required for future robotic space exploration missions. Recent generative-AI methods learn semantically aware navigation…
Reduced-Order Model Guided Contact-Implicit Model Predictive Control for Humanoid Locomotion
Sergio A. Esteban, Vince Kurtz, Adrian B. Ghansah +1
Humanoid robots have great potential for real-world applications due to their ability to operate in environments built for humans, but their deployment is hindered by the challenge…
Generative Predictive Control: Flow Matching Policies for Dynamic and Difficult-to-Demonstrate Tasks
Vince Kurtz, Joel W. Burdick
Generative control policies have recently unlocked major progress in robotics. These methods produce action sequences via diffusion or flow matching, with training data provided by…
Equality Constrained Diffusion for Direct Trajectory Optimization
Vince Kurtz, Joel W. Burdick
The recent success of diffusion-based generative models in image and natural language processing has ignited interest in diffusion-based trajectory optimization for nonlinear contr…
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…
Inverse Dynamics Trajectory Optimization for Contact-Implicit Model Predictive Control
Vince Kurtz, Alejandro Castro, Aykut Özgün Önol +1
Robots must make and break contact with the environment to perform useful tasks, but planning and control through contact remains a formidable challenge. In this work, we achieve r…
Temporal Logic Motion Planning with Convex Optimization via Graphs of Convex Sets
Vince Kurtz, Hai Lin
Temporal logic is a concise way of specifying complex tasks. But motion planning to achieve temporal logic specifications is difficult, and existing methods struggle to scale to co…
Contact-Implicit Trajectory Optimization with Hydroelastic Contact and iLQR
Vince Kurtz, Hai Lin
Contact-implicit trajectory optimization offers an appealing method of automatically generating complex and contact-rich behaviors for robot manipulation and locomotion. The scalab…
Control Barrier Functions for Singularity Avoidance in Passivity-Based Manipulator Control
Vince Kurtz, Patrick M. Wensing, Hai Lin
Task-space Passivity-Based Control (PBC) for manipulation has numerous appealing properties, including robustness to modeling error and safety for human-robot interaction. Existing…
Mini Cheetah, the Falling Cat: A Case Study in Machine Learning and Trajectory Optimization for Robot Acrobatics
Vince Kurtz, He Li, Patrick M. Wensing +1
Seemingly in defiance of basic physics, cats consistently land on their feet after falling. In this paper, we design a controller that lands the Mini Cheetah quadruped robot on its…
Trajectory Optimization for High-Dimensional Nonlinear Systems under STL Specifications
Vince Kurtz, Hai Lin
Signal Temporal Logic (STL) has gained popularity in recent years as a specification language for cyber-physical systems, especially in robotics. Beyond being expressive and easy t…
Approximate Simulation for Template-Based Whole-Body Control
Vince Kurtz, Patrick M. Wensing, Hai Lin
Reduced-order template models are widely used to control high degree-of-freedom legged robots, but existing methods for template-based whole-body control rely heavily on heuristics…
Formal Connections between Template and Anchor Models via Approximate Simulation
Vince Kurtz, Rafael Rodrigues da Silva, Patrick M. Wensing +1
Reduced-order template models like the Linear Inverted Pendulum (LIP) and Spring-Loaded Inverted Pendulum (SLIP) are widely used tools for controlling high-dimensional humanoid rob…
Bayesian Optimization for Polynomial Time Probabilistically Complete STL Trajectory Synthesis
Vince Kurtz, Hai Lin
In recent years, Signal Temporal Logic (STL) has gained traction as a practical and expressive means of encoding control objectives for robotic and cyber-physical systems. The stat…
Toward Verifiable Real-Time Obstacle Motion Prediction for Dynamic Collision Avoidance
Vincent Kurtz, Hai Lin
Next generation Unmanned Aerial Vehicles (UAVs) must reliably avoid moving obstacles. Existing dynamic collision avoidance methods are effective where obstacle trajectories are lin…