ﻻ يوجد ملخص باللغة العربية
While most robotics simulation libraries are built for low-dimensional and intrinsically serial tasks, soft-body and multi-agent robotics have created a demand for simulation environments that can model many interacting bodies in parallel. Despite the increasing interest in these fields, no existing simulation library addresses the challenge of providing a unified, highly-parallelized, GPU-accelerated interface for simulating large robotic systems. Titan is a versatile CUDA-based C++ robotics simulation library that employs a novel asynchronous computing model for GPU-accelerated simulations of robotics primitives. The innovative GPU architecture design permits simultaneous optimization and control on the CPU while the GPU runs asynchronously, enabling rapid topology optimization and reinforcement learning iterations. Kinematics are solved with a massively parallel integration scheme that incorporates constraints and environmental forces. We report dramatically improved performance over CPU-based baselines, simulating as many as 300 million primitive updates per second, while allowing flexibility for a wide range of research applications. We present several applications of Titan to high-performance simulations of soft-body and multi-agent robots.
In this report for the Nasa NIAC Phase I study, we present a mission architecture and a robotic platform, the Shapeshifter, that allow multi-domain and redundant mobility on Saturns moon Titan, and potentially other bodies with atmospheres. The Shape
Soft robotics is an emerging field of research where the robot body is composed of compliant and soft materials. It allows the body to bend, twist, and deform to move or to adapt its shape to the environment for grasping, all of which are difficult f
Deep Learning has revolutionized our ability to solve complex problems such as Vision-and-Language Navigation (VLN). This task requires the agent to navigate to a goal purely based on visual sensory inputs given natural language instructions. However
Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse pro
Practical aperture synthesis imaging algorithms work by iterating between estimating the sky brightness distribution and a comparison of a prediction based on this estimate with the measured data (visibilities). Accuracy in the latter step is crucial