arXiv:2607.00442v1 Announce Type: cross Abstract: Reinforcement learning (RL) for quadruped locomotion commonly depends on fixed, hand-crafted, and Markovian reward functions that limit both interpretability of learned policies and lack explicit control over gait behaviors. We introduce a framework where distinct gaits are specified using parameterized constraints expressed in Signal Temporal Logic (STL). These include safety bounds, gait synchronization constraints, command tracking, and actuation bounds. From these specifications, we develop a reward shaping mechanism that provides learning
The continuous advancements in reinforcement learning and the increasing demand for more reliable and interpretable robotic locomotion systems are driving this innovation.
This research provides a more robust and interpretable method for controlling complex robot gaits, addressing key limitations in current reinforcement learning approaches for robotics.
Robot locomotion policies can now be explicitly designed with parameterized constraints, leading to safer, more predictable, and more adaptable quadruped behaviors than purely reward-based systems.
- · Robotics researchers
- · Quadruped robot manufacturers
- · Logistics and inspection sectors
- · Developers relying solely on black-box RL
- · Systems with high failure rates due to unconstrained control
More sophisticated and reliable autonomous robotic systems will emerge across various industries.
Improved control and interpretability will accelerate the deployment of quadruped robots in unpredictable or hazardous environments.
The methodology could generalize to other complex autonomous systems, enhancing safety and performance beyond locomotion.
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Read at arXiv cs.AI