19 citations · 22 across the 15 of their papers we have counts for
20 papers
MR. POP: Multi-Robot Parallel Optimizing Planner for Almost-Surely Asymptotically Optimal Planning
Chih H. Huang, Roy Xing, Brian Plancher +1
Finding globally optimal paths remains a fundamental challenge in multi-robot motion planning. Despite acceleration of almost-surely asymptotically optimal (a.s.a.o.) planners via…
RAYA: Learning Where and When to Intervene for Robot Recovery
Ishaan Mahajan, Charles Chen, Frederike Dümbgen +1
A robot can predict failure and still be unable to prevent it. By the time a safety mechanism reacts, the nominal plan may already have spent the control authority that recovery re…
AccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny Drones
Andrea Grillo, Brian Plancher
Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, although recent cached model pred…
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…
Real-Time, Energy-Efficient, Sampling-Based Optimal Control via FPGA Acceleration
Tanmay Desai, Brian Plancher, R. Iris Bahar
Autonomous mobile robots (AMRs), used for search-and-rescue and remote exploration, require fast and robust planning and control schemes. Sampling-based approaches for Model Predic…
Robust Geospatial Coordination of Multi-Agent Communications Networks Under Attrition
Jonathan S. Kent, Eliana Stefani, Brian Plancher
Coordinating emergency responses in extreme environments, such as wildfires, requires resilient and high-bandwidth communication backbones. While autonomous aerial swarms can estab…
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…
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…
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…
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…
Robust and Efficient Embedded Convex Optimization through First-Order Adaptive Caching
Ishaan Mahajan, Brian Plancher
Recent advances in Model Predictive Control (MPC) leveraging a combination of first-order methods, such as the Alternating Direction Method of Multipliers (ADMM), and offline preco…
Set Phasers to Stun: Beaming Power and Control to Mobile Robots with Laser Light
Charles J. Carver, Hadleigh Schwartz, Toma Itagaki +7
We present Phaser, a flexible system that directs narrow-beam laser light to moving robots for concurrent wireless power delivery and communication. We design a semi-automatic cali…
Polynomial and Parallelizable Preconditioning for Block Tridiagonal Positive Definite Matrices
Shaohui Yang, Toshiyuki Ohtsuka, Brian Plancher +1
The efficient solution of moderately large-scale linear systems arising from the KKT conditions in optimal control problems (OCPs) is a critical challenge in robotics. With the sta…
pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning
Chih H. Huang, Pranav Jadhav, Brian Plancher +1
Sampling-based motion planning algorithms, like the Rapidly-Exploring Random Tree (RRT) and its widely used variant, RRT-Connect, provide efficient solutions for high-dimensional p…
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…
Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning
Lev Grossman, Brian Plancher
Perceptive deep reinforcement learning (DRL) has lead to many recent breakthroughs for complex AI systems leveraging image-based input data. Applications of these results range fro…
TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
Anoushka Alavilli, Khai Nguyen, Sam Schoedel +2
Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is o…
MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU
Emre Adabag, Miloni Atal, William Gerard +1
Nonlinear Model Predictive Control (NMPC) is a state-of-the-art approach for locomotion and manipulation which leverages trajectory optimization at each control step. While the per…
Symmetric Stair Preconditioning of Linear Systems for Parallel Trajectory Optimization
Xueyi Bu, Brian Plancher
There has been a growing interest in parallel strategies for solving trajectory optimization problems. One key step in many algorithmic approaches to trajectory optimization is the…
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion
Lev Grossman, Brian Plancher
Deep reinforcement learning (DRL) is one of the most powerful tools for synthesizing complex robotic behaviors. But training DRL models is incredibly compute and memory intensive,…