From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
Summary: arXiv:2604.12474v1 Announce Type: 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 resulting plan respects the robot’s true physical constraints. Consequently, even when the high-level action sequence is fixed, producing a dynamically feasible trajectory becomes a bi-level optimization problem.
Introduction
Robotics is a rapidly evolving field, and the ability to navigate complex environments is crucial for the successful deployment of robotic agents. Traditional methods often rely on simplified models that fail to capture the intricacies of real-world physics. As a result, there is a growing need for advanced planning techniques that can ensure the feasibility of trajectories while adhering to various constraints.
Challenge of Hybrid Planning
The challenge lies in the mixed discrete-continuous nature of robotic tasks. Agents must not only plan a sequence of discrete actions but also determine a continuous trajectory that adheres to physical laws. The complexity increases when considering constraints such as:
- Deadlines for task completion
- Time windows for specific actions
- Velocity limits
- Acceleration limits
Current hybrid planners often utilize linear dynamics, which can lead to plans that are not physically feasible. This gap necessitates additional optimization steps to adjust the initial plans into viable trajectories.
Proposed Solution
To address these issues, we propose a novel approach using reinforcement learning in continuous space. Our method defines a Markov Decision Process (MDP) that explicitly incorporates second-order analytical constraints, allowing for the refinement of first-order plans generated by hybrid planners.
This approach enables the adjustment of trajectories to meet the physical constraints of the robotic agents, thereby enhancing the reliability of the plans produced. By leveraging reinforcement learning, we can iteratively improve the trajectory to ensure it is dynamically feasible.
Results and Discussion
Our experiments demonstrate that this method can effectively bridge the gap between first-order trajectory planning and the dynamics required for real execution. The results indicate that the refined plans not only meet the required constraints but also improve the overall efficiency of the execution process.
By integrating second-order dynamics into the planning process, we are able to provide a more robust framework for robotic navigation, enabling agents to perform tasks in complex environments with greater reliability.
Conclusion
The integration of reinforcement learning with traditional planning methods represents a significant advancement in the field of robotics. As robotic systems continue to evolve, the need for reliable and physically feasible planning techniques will only grow. Our approach offers a promising direction for future research, with the potential to enhance the capabilities of robotic agents in various applications.