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

• URL: http://arxiv.org/abs/2604.13323v1
• Date: Tue, 14 Apr 2026 22:00:53 GMT
• Title: Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control
• Abstract summary: We present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization.<n>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.
• Score: 2.940492773889387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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