Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

Austin, Jacob; Corrales-Fatou, Rafael; Wyetzner, Sofia; Lipson, Hod

doi:10.1109/icra40945.2020.9196808

Cited by 19 publications

(16 citation statements)

References 13 publications

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“…For a higher fidelity simulation, deformation must be taken into account. We developed a soft robot simulation environment based on the Titan [22], which is a CUDA [36] accelerated massively parallel asynchronous spring-mass simulation library. We extended the Titan library with a rotational kernel for contact-coupling of soft bodies.…”

Section: B Soft-body Simulationmentioning

confidence: 99%

“…Compared to rigid body simulation, soft body simulation has a much higher number of degrees of freedom and thus is more computationally expensive. Several methods are suitable for large scale real-time soft body simulation: The Titan Simulator [22] is a CUDA accelerated spring-mass-based soft body simulation engine. The Nvidia Flex [23] is a simulation engine using position-based dynamics with unified particle representation.…”

Section: Introductionmentioning

confidence: 99%

“…Locomotions in various terrains were tested, as will be shown in the following section. In addition, we developed and verified a real-time soft body simulation based on Titan [22], which enabled rapid design improvements and gait tuning.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Legged Soft Robot Platform for Dynamic Locomotion

Xia

Zhu

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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We present an open-source untethered quadrupedal soft robot platform for dynamic locomotion (e.g., high-speed running and backflipping). The robot is mostly soft (80 vol.%) while driven by four geared servo motors. The robot's soft body and soft legs were 3D printed with gyroid infill using a flexible material, enabling it to conform to the environment and passively stabilize during locomotion on multi-terrain environments. In addition, we simulated the robot in a real-time soft body simulation. With tuned gaits in simulation, the real robot can locomote at a speed of 0.9 m/s (2.5 body length/second), substantially faster than most untethered legged soft robots published to date. We hope this platform, along with its verified simulator, can catalyze the development of soft robotics.

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Section: B Soft-body Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Legged Soft Robot Platform for Dynamic Locomotion

Xia

Zhu

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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“…Using automatic differentiation, differentiable physics engines can support gradient computations and have shown promise for real-time nonlinear MPC use on CPUs [29] and for accelerating machine learning, computer graphics, and soft robotics applications [30], [31], [32], [33], [34], [35], [24], [36] on CPUs and GPUs. However, existing GPUbased differentiable physics engines are optimized for simulating thousands of interacting bodies through contact using maximal coordinate, particle, and mesh-based approaches, which are less accurate when used for rigid body robotics applications over longer time step durations [21], [24].…”

Section: Related Workmentioning

confidence: 99%

GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients

Plancher¹,

Neuman²,

Ghosal³

et al. 2021

Preprint

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We introduce GRiD: a GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate the nonlinear trajectory optimization subproblem used in state-of-the-art robotic planning, control, and machine learning. Each iteration of nonlinear trajectory optimization requires tens to hundreds of naturally parallel computations of rigid body dynamics and their gradients. GRiD leverages URDF parsing and code generation to deliver optimized dynamics kernels that not only expose GPUfriendly computational patterns, but also take advantage of both fine-grained parallelism within each computation and coarsegrained parallelism between computations. Through this approach, when performing multiple computations of rigid body dynamics algorithms, GRiD provides as much as a 7.6x speedup over a state-of-the-art, multi-threaded CPU implementation, and maintains as much as a 2.6x speedup when accounting for I/O overhead. We release GRiD as an open-source library, so that it can be leveraged by the robotics community to easily and efficiently accelerate rigid body dynamics on the GPU.

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“…Recent work has seen an explosion of specialized robotics acceleration on nontraditional computing platforms such as GPUs, FPGAs, and ASICs [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. This has been sparked by the decline of Moore's Law and Dennard Scaling, which limits the performance of traditional CPU computing, positioning hardware acceleration as an emerging solution to achieve high performance and power efficiency in robotics applications.…”

Section: Introductionmentioning

confidence: 99%

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman²,

Plancher³

et al. 2022

Preprint

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Hardware acceleration can revolutionize robotics, enabling new applications by speeding up robot response times while remaining power-efficient. However, the diversity of acceleration options makes it difficult for roboticists to easily deploy accelerated systems without expertise in each specific hardware platform. In this work, we address this challenge with 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 use it to deploy a ROS 2 perception computational graph on a CPU and FPGA.We employ our integrated tracing and benchmarking to analyze bottlenecks, uncovering insights that guide us to improve FPGA communication efficiency. In particular, 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.

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Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

Cited by 19 publications

References 13 publications

A Legged Soft Robot Platform for Dynamic Locomotion

A Legged Soft Robot Platform for Dynamic Locomotion

GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

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