SymForce: Symbolic Computation and Code Generation for Robotics
원문 초록
We present SymForce, a library for fast symbolic computation, code generation, and nonlinear optimization for robotics applications like computer vision, motion planning, and controls. SymForce combines the development speed and flexibility of symbolic math with the performance of autogenerated, highly optimized code in C++ or any target runtime language. SymForce provides geometry and camera types, Lie group operations, and branchless singularity handling for creating and analyzing complex symbolic expressions in Python, built on top of SymPy. Generated functions can be integrated as factors into our tangent-space nonlinear optimizer, which is highly optimized for real-time production use. We introduce novel methods to automatically compute tangent-space Jacobians, eliminating the need for bug-prone handwritten derivatives. This workflow enables faster runtime code, faster development time, and fewer lines of handwritten code versus the state-of-the-art. Our experiments demonstrate that our approach can yield order of magnitude speedups on computational tasks core to robotics. Code is available at https://github.com/symforce-org/symforce.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
EvoAgentX: An Automated Framework for Evolving Agentic Workflows
ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs
LLM Performance for Code Generation on Noisy Tasks
Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test
Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team
토론
이 논문과 연결된 포럼 글이 아직 없습니다.