arXiv:2603.16809v2 Announce Type: replace-cross Abstract: Behavior Trees (BTs) offer a powerful paradigm for designing modular and reactive robot controllers. BT planning, an emerging field, provides theoretical guarantees for the automated generation of reliable BTs. However, BT planning typically assumes that a well-designed BT system is already grounded -- comprising high-level action models and low-level control policies -- which often requires extensive expert knowledge and manual effort. In this paper, we formalize the BT Grounding problem: the automated construction of a complete and co
The increasing complexity and demand for autonomous robot systems necessitate more efficient and less labor-intensive methods for programming and control. Advances in AI foundational models are making automated grounding of complex robot behaviors feasible.
Automating the 'grounding' process for robot behavior trees significantly lowers the barrier to entry for complex robotic applications, accelerating deployment and capabilities across various industries. This reduces the need for extensive expert knowledge, making advanced robotics more accessible.
Robot programming shifts from heavily manual, expert-driven configuration to more automated and generalized abstract models, speeding up development and enabling robots to adapt to new tasks more readily. This fundamentally changes how robots are deployed and scaled.
- · robotics manufacturers
- · logistics and automation sectors
- · AI software developers
- · research institutions
- · manual robot programmers
- · companies reliant on bespoke, costly robot deployments
More sophisticated and adaptable robots can be deployed faster and at a lower cost across a wider range of applications.
This improved deployability leads to increased automation in manufacturing, logistics, and service industries, boosting productivity.
The widespread adoption of highly autonomous and context-aware robots could reshape labor markets and demand new forms of human-robot collaboration.
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Read at arXiv cs.AI