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arXiv · 2604.13323

Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control

Shrutheesh R Iyer·I-Chia Chang·Andrew Z. Liu·Yan Gu·Zachary Kingston
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Abstract

Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorithms are capable of planning collision-free path between start and goal configurations on the constraint submanifolds specified by task. Current state-of-the-art methods can take tens of seconds to solve these tasks for complex systems such as humanoid robots, making real-world use impractical, especially in dynamic settings. Inspired by recent advances in hardware accelerated motion planning, we present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization. By transforming relevant components into parallelizable structures, we use SIMD parallelism to plan constraint satisfying solutions. Our approach achieves up to 100-1000x speed-ups over the state-of-the-art, making real-time constrained motion planning feasible for the first time. We demonstrate our planner on a real humanoid robot and show real-time whole-body quasi-static plan generation. Our work is available at https://commalab.org/papers/mcvamp/.

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Shrutheesh R Iyer, I-Chia Chang, Andrew Z. Liu, Yan Gu, Zachary Kingston. 2026-04-14. Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control. https://arxiv.org/abs/2604.13323

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