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 common baseline. The suite encompasses ROS 2 packages covering the full robotics pipeline and integrates two distinct benchmarking approaches: black-box testing, which measures performance by eliminating upper layers and replacing them with a test application, and grey-box testing, an application-specific measure that observes internal system states with minimal interference. Our benchmarking framework provides ready-to-use tools and is easily adaptable for the assessment of custom ROS 2 computational graphs. Drawing from the knowledge of leading robot architects and system architecture experts, RobotPerf establishes a standardized approach to robotics benchmarking. As an open-source initiative, RobotPerf remains committed to evolving with community input to advance the future of hardware-accelerated robotics.
We present RoboShape, an accelerator framework that leverages two topology-based computational patterns that scale with robot size: (1) topology traversals, and (2) large topology-based matrices. Using these patterns and building on prior work, we expose opportunities to directly use robot topology to inform architectural mechanisms including task scheduling and allocation, data placement, block matrix operations, and sparse I/O data. For the topologically-diverse iiwa manipulator, HyQ quadruped, and Baxter torso robots, RoboShape accelerators on an FPGA provide a 4.0x to 4.4x speedup in compute latency over CPU and a 8.0x to 15.1x speedup over GPU for the dynamics gradients, a key bottleneck preventing online execution of nonlinear optimal motion control for legged robots. Taking a broader view, for topology-based applications, RoboShape enables analysis of performance and resource utilization tradeoffs that will be critical to managing resources across accelerators in future full robotics domain-specific SoCs.
We introduce RobotCore, an architecture to integrate hardware acceleration in the widely-used ROS 2 robotics software framework. This architecture is target-agnostic (supports edge, workstation, data center, or cloud targets) and accelerator-agnostic (supports both FPGAs and GPUs). It builds on top of the common ROS 2 build system and tools and is easily portable across different research and commercial solutions through a new firmware layer. We also leverage the Linux Tracing Toolkit next generation (LTTng) for low-overhead real-time tracing and benchmarking. To demonstrate the acceleration enabled by this architecture, we design an intra-FPGA ROS 2 node communication queue to enable faster data flows, and use it in conjunction with FPGA-accelerated nodes to achieve a 24.42% speedup over a CPU.
We introduce and release GRiD, an open-source, GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate nonlinear trajectory optimization through optimized code generation, GRiD provides as much as a 7.2x speedup over a state-of-the-art, multi-threaded CPU implementation and maintains as much as a 2.5x speedup when accounting for I/O overhead.
We introduce robomorphic computing; a methodology to transform robot morphology into a customized hardware accelerator morphology. In this work, we (i) present this design methodology; (ii) use the methodology to generate a parameterized accelerator design for the gradient of rigid body dynamics; (iii) evaluate FPGA and synthesized ASIC implementations; and (iv) describe how the design can be automatically customized for other robot models. Our FPGA accelerator achieves speedups of 8x and 86x over CPU and GPU latency, and maintains an overall speedup of 1.9x to 2.9x deployed in an end-to-end coprocessor system. ASIC synthesis indicates an additional factor of 7.2x.
In this paper, we detail the designs of three faster than state-of-the-art implementations of the gradient of rigid body dynamics on a CPU, GPU, and FPGA. Our optimized FPGA and GPU implementations provide as much as a 3.0x end-to-end speedup over our optimized CPU implementation by refactoring the algorithm to exploit its computational features, e.g., parallelism at different granularities.