From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
arXiv:2604.12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission. Such problems are inherently mixed discrete-continuous: a high-level action sequence and a physically feasible continuous trajectory. The resulting trajectory and action sequence must also satisfy problem constraints such as deadlines, time windows, and velocity or acceleration limits. While hybrid temporal planners attempt to address this challenge, they typically model motion using linear (first-order) dynamics, which cannot guarantee that
The continuous advancements in AI and robotics, coupled with the need for more complex and reliable automated systems, are driving research into sophisticated hybrid planning that accounts for physical realities.
This development addresses a critical limitation in current robotic autonomy, allowing for more robust and capable physical agents that can operate reliably in complex, real-world environments.
Robotic systems will be able to perform physically demanding tasks with greater precision and reliability, moving beyond simple kinematic planning to incorporate real-world dynamics and constraints.
- · Robotics industry
- · Logistics and manufacturing
- · Defense contractors
- · AI researchers in motion planning
- · Companies reliant on simple automation methods
- · Manual labor in dangerous/repetitive physical tasks
Robots will become more adept at complex physical manipulation and navigation, enabling deployment in new, demanding applications.
Improved physical autonomy will accelerate the development and adoption of general-purpose robots in various industries, leading to increased productivity and efficiency.
The enhanced capabilities of physically aware robots could lead to new forms of human-robot collaboration and potentially reshape workforces in physically demanding sectors.
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Read at arXiv cs.AI