17 citations · 43 across the 15 of their papers we have counts for
18 papers
pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation
Brian Plancher
GPU acceleration is now routine across robotics, but cloud-hosted GPU continuous integration (CI) runners are expensive, resulting in severe under-testing of GPU-accelerated code.…
GLASS: Architecture-Tuned, Composable, Device-Side Linear Algebra for Edge Robotics and Beyond
Brian Plancher
GPU robotics lacks the reusable numerical infrastructure of mature CPU stacks, instead relying on compiler frameworks that introduce overhead or repeatedly reimplementing numerical…
MPC-Injection: Biasing Off-Policy Locomotion RL Toward Controller-Induced Behavior Basins
Roy Xing, Seyoung Ree, Brian Plancher
Reinforcement learning (RL) for locomotion frequently converges to locally optimal but undeployable behaviors, such as vibrating limbs or scooting on the torso, that maximize retur…
Parallel Dynamic Programming for Conic Linear Quadratic Control
Luyao Zhang, Gabriel Bravo-Palacios, Brian Plancher +1
Linear Quadratic (LQ) control problems are at the heart of linear control theory and Model Predictive Control (MPC). While performant, standard approaches to solving such problems…
TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU
Gabriel Bravo-Palacios, Jianghan Zhang, Zachary Pestrikov +2
Robotics increasingly relies on GPUs for parallel simulation, large-scale learning, and neural-network inference. For model predictive control (MPC) to scale with this paradigm, so…
ASCII Art Turns LLMs into VLA Controllers
Yitao Jiang, Roy Xing, Luyang Zhao +3
Vision--Language--Action (VLA) controllers are often built by extending vision--language models (VLMs) with action supervision, relying on multimodal backbones with large data and…
The Magnificent Seven Challenges and Opportunities in Domain-Specific Accelerator Design for Autonomous Systems
Sabrina M. Neuman, Brian Plancher, Vijay Janapa Reddi
The end of Moore's Law and Dennard Scaling has combined with advances in agile hardware design to foster a golden age of domain-specific acceleration. However, this new frontier of…
Materiality and Risk in the Age of Pervasive AI Sensors
Mona Sloane, Emanuel Moss, Susan Kennedy +4
Artificial intelligence (AI) systems connected to sensor-laden devices are becoming pervasive, which has notable implications for a range of AI risks, including to privacy, the env…
RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance
Víctor Mayoral-Vilches, Jason Jabbour, Yu-Shun Hsiao +16
We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its com…
Datasheets for Machine Learning Sensors
Matthew Stewart, Yuke Zhang, Pete Warden +12
Machine learning (ML) is becoming prevalent in embedded AI sensing systems. These "ML sensors" enable context-sensitive, real-time data collection and decision-making across divers…
Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers
Shvetank Prakash, Matthew Stewart, Colby Banbury +4
The sustained growth of carbon emissions and global waste elicits significant sustainability concerns for our environment's future. The growing Internet of Things (IoT) has the pot…
Machine Learning Sensors
Pete Warden, Matthew Stewart, Brian Plancher +6
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from c…
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
Sabrina M. Neuman, Brian Plancher, Bardienus P. Duisterhof +8
Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the…
RobotCore: An Open Architecture for Hardware Acceleration in ROS 2
Víctor Mayoral-Vilches, Sabrina M. Neuman, Brian Plancher +1
Hardware acceleration can revolutionize robotics, enabling new applications by speeding up robot response times while remaining power-efficient. However, the diversity of accelerat…
GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients
Brian Plancher, Sabrina M. Neuman, Radhika Ghosal +2
We introduce GRiD: a GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate the nonlinear trajectory optimization subp…
RoboRun: A Robot Runtime to Exploit Spatial Heterogeneity
Behzad Boroujerdian, Radhika Ghosal, Jonathan Cruz +2
The limited onboard energy of autonomous mobile robots poses a tremendous challenge for practical deployment. Hence, efficient computing solutions are imperative. A crucial shortco…
Widening Access to Applied Machine Learning with TinyML
Vijay Janapa Reddi, Brian Plancher, Susan Kennedy +21
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloe…
The Role of Compute in Autonomous Aerial Vehicles
Behzad Boroujerdian, Hasan Genc, Srivatsan Krishnan +7
Autonomous-mobile cyber-physical machines are part of our future. Specifically, unmanned-aerial-vehicles have seen a resurgence in activity with use-cases such as package delivery.…