13 papers
HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent
Cael Yasutake, Andrew H. Liu, Zachary Kingston +1
Inverse Kinematics (IK) is a core problem in robotics, in which joint configurations are found to achieve a (collision-free) desired end-effector pose. Modern IK solvers face a fun…
TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo +4
Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existing solvers are too computatio…
GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
Alexander Du, Emre Adabag, Gabriel Bravo-Palacios +1
While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear trajectory optimization (TO) problems o…
TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
Shuo Sha, Anupam Bhakta, Zhenyuan Jiang +4
Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time adjustment to payload changes, environmental interactions, and system we…
Code Generation and Conic Constraints for Model-Predictive Control on Microcontrollers with Conic-TinyMPC
Ishaan Mahajan, Khai Nguyen, Sam Schoedel +4
Model-predictive control (MPC) is a state-of-the-art control method for constrained robotic systems, yet deployment on resource-limited hardware remains difficult. This challenge i…
Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size
Thomas Lew, Marcus Greiff, John Subosits +1
Proximal methods such as the Alternating Direction Method of Multipliers (ADMM) are effective at solving constrained quadratic programs (QPs). To tackle infeasible QPs, slack varia…