arXiv:2606.26700v1 Announce Type: cross Abstract: Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation. A major bottleneck for this problem in cluttered environments is that infeasible planning attempts by Sampling-based motion planners (SBMPs) can incur substantial computational cost. Also existing approaches for infeasibility certification are limited to low-dimensional configuration spaces and often assume simplified geometric environments represented by primitive objects with known parameters. We study the complementary p
The increasing complexity of robotic tasks in dynamic, cluttered environments necessitates more efficient and robust motion planning solutions.
Improved motion feasibility prediction enhances the autonomy and efficiency of robots, reducing computational bottlenecks and expanding their application in real-world scenarios.
Robots will be able to navigate and manipulate objects more effectively in complex environments, accelerating their deployment in manufacturing, logistics, and hazardous operations.
- · Robotics manufacturers
- · Logistics and automation companies
- · AI software developers
- · Manual labor in cluttered environments
- · Companies relying on less efficient robotic planning
More widespread and effective deployment of robots in previously challenging environments.
Increased automation across various industries, leading to productivity gains and reshaped labor markets.
Enhanced human-robot collaboration as robots become more adept at navigating shared spaces.
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Read at arXiv cs.AI