mlsanityData to 14 Oct 2026

LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts

arXiv 2605.05110 ↗First posted 6 May 2026cs.RO cs.AIcited by 1

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Designing reward functions for agile robotic maneuvers in reinforcement learning remains difficult, and demonstration-based approaches often require reference motions that are unavailable for novel platforms or extreme stunts. We present LineRides, a line-guided learning framework that enables a custom bicycle robot to acquire diverse, commandable stunt behaviors from a user-provided spatial guideline and sparse key-orientations, without demonstrations or explicit timing. LineRides handles physically infeasible guidelines using a tracking margin that permits controlled deviation, resolves temporal ambiguity by measuring progress via traveled distance along the guideline, and disambiguates motion details through position- and sequence-based key-orientations. We evaluate LineRides on the Ultra Mobility Vehicle (UMV) and show that the policy trained with our methods supports seamless transitions between normal driving and stunt execution, enabling five distinct stunts on command: MiniHop, LargeHop, ThreePointTurn, Backflip, and DriftTurn.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 31 of its 43 references that are in the map. First posted on arXiv 6 May 2026.

Builds on

31 of 43 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026Flip Stunts on Bicycle Robots using Iterative Motion Imitation30 Mar
  2. 2025Guided Reinforcement Learning for Omnidirectional 3D Jumping in Quadruped Robots22 Jul
  3. 2025High-Performance Reinforcement Learning on Spot: Optimizing Simulation Parameters with Distributional Measures24 Apr
  4. 2025Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches14 Mar
  5. 2024Waypoint-Based Reinforcement Learning for Robot Manipulation Tasks20 Mar
  6. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control30 Jan

Other references · 25

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  4. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 251
  5. 2024OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learningcited by 189
  6. 2022Rapid Locomotion via Reinforcement Learningcited by 168
  7. 2023Extreme Parkour with Legged Robotscited by 164
  8. 2023ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robotscited by 152
  9. 2023Robot Parkour Learningcited by 149
  10. 2023RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketchescited by 133
  11. 2025ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skillscited by 130
  12. 2024MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpaintingcited by 116
13 more
  1. 2023CALM: Conditional Adversarial Latent Models for Directable Virtual Characterscited by 94
  2. 2025GMT: General Motion Tracking for Humanoid Whole-Body Controlcited by 83
  3. 2025KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skillscited by 52
  4. 2022Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrationscited by 45
  5. 2025Attention-Based Map Encoding for Learning Generalized Legged Locomotioncited by 40
  6. 2023Robust and Versatile Bipedal Jumping Control through Reinforcement Learningcited by 36
  7. 2025High-speed control and navigation for quadrupedal robots on complex and discrete terraincited by 19
  8. 2024RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewardscited by 18
  9. 2023Instructing Robots by Sketching: Learning from Demonstration via Probabilistic Diagrammatic Teachingcited by 14
  10. 2025Unsupervised Skill Discovery as Exploration for Learning Agile Locomotioncited by 6
  11. 2025L2D2: Robot Learning from 2D Drawingscited by 4
  12. 2026System Design of the Ultra Mobility Vehicle: A Driving, Balancing, and Jumping Bicycle Robotcited by 4
  13. 2025Reference Grounded Skill Discoverycited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Bicycle Acrobatics with Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach
  2. 2026Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair
  3. 2024OGMP: Oracle Guided Multi-mode Policies for Agile and Versatile Robot Control
  4. 2025Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
  5. 2026MimicAgent: Quadruped Skills via Prompt-to-Trajectory Generation
  6. 2026AGILE: A Comprehensive Workflow for Humanoid Loco-Manipulation Learning
  7. 2026Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot
  8. 2026CycleRL: Sim-to-Real Deep Reinforcement Learning for Robust Autonomous Bicycle Control