LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts

Problem
This paper addresses the challenge of designing effective reward functions for agile robotic maneuvers in reinforcement learning (RL), particularly for novel platforms and extreme stunts where demonstration-based approaches are impractical. The authors propose a framework, LineRides, that allows a bicycle robot to learn diverse stunt behaviors using user-provided spatial guidelines and sparse key-orientations, without the need for explicit demonstrations or timing. This work is presented as a preprint and has not yet undergone peer review.

Method
LineRides introduces a line-guided learning framework that incorporates several innovative components. The architecture leverages a tracking margin to handle physically infeasible guidelines, allowing the robot to deviate from the guideline while maintaining control. Temporal ambiguity is resolved by measuring progress based on the distance traveled along the guideline, rather than relying on fixed time intervals. Additionally, the framework employs position- and sequence-based key-orientations to disambiguate the details of the robot’s motion. The training process involves reinforcement learning with a custom reward function that encourages the robot to follow the guideline while executing stunts. The authors do not disclose specific details regarding the training compute or the exact architecture used.

Results
The effectiveness of LineRides is evaluated on the Ultra Mobility Vehicle (UMV), demonstrating the ability to perform five distinct stunts: MiniHop, LargeHop, ThreePointTurn, Backflip, and DriftTurn. The trained policy allows for seamless transitions between normal driving and stunt execution. While specific quantitative results (e.g., success rates, performance metrics) are not provided in the abstract, the authors claim that the framework enables commandable stunt behaviors that were previously difficult to achieve with traditional RL methods.

Limitations
The authors acknowledge that the framework may struggle with highly complex or unpredictable environments where the spatial guidelines may not be sufficient for effective learning. Additionally, the reliance on user-provided guidelines may limit the generalizability of the learned behaviors to unstructured scenarios. The paper does not discuss the scalability of the approach to other robotic platforms or the potential computational overhead associated with the tracking margin and key-orientation mechanisms.

Why it matters
LineRides has significant implications for the field of robotic control and reinforcement learning, particularly in applications requiring agile maneuvers and stunt execution. By enabling robots to learn from spatial guidelines rather than demonstrations, this framework opens avenues for training in environments where traditional methods are infeasible. The ability to command diverse behaviors on demand could enhance the versatility of robotic systems in real-world applications, such as entertainment, search and rescue, and autonomous navigation. Future work could explore the extension of this framework to other robotic platforms and more complex environments, as well as the integration of additional sensory inputs to improve robustness.

Authors: Seungeun Rho, Shamel Fahmi, Jeonghwan Kim, Arianna Ilvonen, Sehoon Ha, Gabriel Nelson
Source: arXiv:2605.05110
URL: https://arxiv.org/abs/2605.05110v1