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SkipVLA is a hybrid policy that combines a pretrained VLA with a classical motion planner, using the planner for free-space motion and querying the VLA only for contact-rich skills such as grasping and placing, demonstrating up to 2.5x faster task completion and significantly lower energy consumption.
A decentralized approach using neural HJR learning achieves superior safety and efficiency in multi-arm motion planning, outperforming traditional methods.
Motion planning just got a whole lot faster: OMPL 2.0 brings hardware acceleration to your robot's pathfinding.
Real-time constrained motion planning for complex robots is now feasible, thanks to a 100-1000x speedup achieved through SIMD parallelization of projection-based constraint satisfaction.
Kinodynamic motion planning just got a whole lot faster: AkinoPDF achieves microsecond-level planning times by exploiting differential flatness for analytical solutions.
