arXiv:2504.16738v3 Announce Type: replace-cross Abstract: Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle novel tasks by flexibly composing generic skills. Solutions to this problem lie in an infinitely vast space of parameterized skill sequences -- a space where common incremental methods struggle to find sequences that have non-obvious intermediate steps. Some approaches reason over lower-dimensional, symbolic spaces, which are more tractable to explore but may
This paper addresses a core challenge in robotics—efficiently planning complex manipulation motions using predefined skills—which is a critical step towards more general-purpose AI and autonomous systems.
Advanced simulation and skill-centric planning could significantly accelerate the development and deployment of flexible robotic systems, impacting various industries and operational efficiencies.
This approach offers a method to overcome limitations in current planning strategies, potentially enabling robots to perform complex tasks with non-obvious intermediate steps, moving beyond simple, pre-programmed actions.
- · Robotics companies
- · Logistics and manufacturing sectors
- · AI software developers
- · Research institutions
- · Manual labor in repetitive manipulation tasks
- · Companies reliant on less flexible automation solutions
Improved robotic efficiency and versatility in complex manipulation tasks.
Accelerated adoption of advanced robotics in new domains due to enhanced task generalization capabilities.
Increased demand for sophisticated physics simulation platforms and skill-centric AI frameworks, further integrating AI into physical world operations.
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Read at arXiv cs.AI