SRL: Combining SLIP Model and Reinforcement Learning for Agile Robotic Jumping

• URL: http://arxiv.org/abs/2606.18625v1
• Date: Wed, 17 Jun 2026 02:41:53 GMT
• Title: SRL: Combining SLIP Model and Reinforcement Learning for Agile Robotic Jumping
• Abstract summary: We propose Spring-loaded Reinforcement Learning (SRL), which integrates SLIP-based feedforward control signals with RL-driven real-time feedback.<n>SRL exhibits robust adaptability to various task requirements and environmental complexities, underscoring its potential for real-world deployment.
• Score: 6.344868277741761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic jumping is pivotal in applications such as search and rescue and logistics, where crossing obstacles and enhancing mobility efficiency are critical. The Spring-Loaded Inverted Pendulum (SLIP) model leverages simplified spring-mass dynamics that naturally encode biologically plausible hopping motions, yet its performance degrades on irregular terrain due to idealized assumptions regarding contact and joint dynamics. Meanwhile, Reinforcement Learning (RL) can adapt to diverse and complex environments but often requires extensive data from unguided exploration. The complementary strengths of SLIP's physically grounded baseline and RL's adaptive capabilities motivate a hybrid framework that overcomes these individual limitations. We therefore propose Spring-loaded Reinforcement Learning (SRL), which integrates SLIP-based feedforward control signals with RL-driven real-time feedback, enabling continuous optimization of robotic jumping. Experimental results demonstrate that SRL can achieve more stable jumps with much less training time than the baseline method, maintaining an average position tracking error below 0.1 m and velocity tracking errors within +/-3% of the target values. Through bipedal and quadrupedal simulations of ground and stair jumping, as well as sim-to-sim and sim-to-real validations, SRL exhibits robust adaptability to various task requirements and environmental complexities, underscoring its potential for real-world deployment.

Related papers

• Learning to Throw: Agile and Accurate Cable-Suspended Payload Delivery with a Quadrotor [17.676294984292433]
  Quadrotors offer the agility needed to rapidly transport suspended payloads during time-critical applications.<n>State-of-the-art approaches typically rely on model-based trajectory optimization and tracking.<n>We propose a hybrid simulation framework that couples a high-fidelity analytical quadrotor model with a physics solver for complex rope and payload interactions.
  arXiv  Detail & Related papers  (2026-06-25T23:30:52Z)
• ASTER: Attitude-aware Suspended-payload Quadrotor Traversal via Efficient Reinforcement Learning [6.13534373131836]
  This paper presents ASTER, a robust RL framework that achieves, to our knowledge, the first successful autonomous inverted flight for the cable-suspended system.<n>We show remarkable agility, precise attitude alignment, and robust zero-shot sim-to-real transfer across complex trajectories.
  arXiv  Detail & Related papers  (2026-03-11T12:40:29Z)
• Taming the Long-Tail: Efficient Reasoning RL Training with Adaptive Drafter [52.111923076688505]
  Training Large Language Models (LLMs) with strong reasoning capabilities marks a significant milestone, unlocking new frontiers in complex problem-solving.<n>We propose TLT, a system that accelerates reasoning RL training losslessly by integrating adaptive speculative decoding.
  arXiv  Detail & Related papers  (2025-11-20T18:59:25Z)
• Scaling Agent Learning via Experience Synthesis [100.42712232390532]
  Reinforcement learning can empower autonomous agents by enabling self-improvement through interaction.<n>But its practical adoption remains challenging due to costly rollouts, limited task diversity, unreliable reward signals, and infrastructure complexity.<n>We introduce DreamGym, the first unified framework designed to synthesize diverse experiences with scalability in mind.
  arXiv  Detail & Related papers  (2025-11-05T18:58:48Z)
• Human-in-the-loop Online Rejection Sampling for Robotic Manipulation [55.99788088622936]
  Hi-ORS stabilizes value estimation by filtering out negatively rewarded samples during online fine-tuning.<n>Hi-ORS fine-tunes a pi-base policy to master contact-rich manipulation in just 1.5 hours of real-world training.
  arXiv  Detail & Related papers  (2025-10-30T11:53:08Z)
• RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking [50.200035833530876]
  RobotDancing is a simple, scalable framework that predicts residual joint targets to explicitly correct dynamics discrepancies.<n>It can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality.
  arXiv  Detail & Related papers  (2025-09-25T03:30:34Z)
• Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator [50.191655141020505]
  Reinforcement Learning (RL) has demonstrated impressive capabilities in robotic control but remains challenging due to high sample complexity, safety concerns, and the sim-to-real gap.<n>We introduce Offline Robotic World Model (RWM-O), a model-based approach that explicitly estimates uncertainty to improve policy learning without reliance on a physics simulator.
  arXiv  Detail & Related papers  (2025-04-23T12:58:15Z)
• Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems [42.2790464348673]
  We propose a multi-fidelity reinforcement learning framework for controlling instabilities in complex systems.<n>The effect of the proposed framework is demonstrated on two complex dynamics in physics.
  arXiv  Detail & Related papers  (2025-04-08T00:50:15Z)
• CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishing [5.467140383171385]
  This work presents an experimental demonstration of the hybrid RL algorithm CHEQ for robotic polishing with variable impedance.<n>On hardware, CHEQ achieves effective polishing behaviour, requiring only eight hours of training and incurring just five failures.<n>Results highlight the potential of adaptive hybrid RL for real-world, contact-rich tasks trained directly on hardware.
  arXiv  Detail & Related papers  (2025-01-14T10:13:41Z)
• Physics Enhanced Residual Policy Learning (PERPL) for safety cruising in mixed traffic platooning under actuator and communication delay [8.172286651098027]
  Linear control models have gained extensive application in vehicle control due to their simplicity, ease of use, and support for stability analysis. Reinforcement learning (RL) models, on the other hand, offer adaptability but suffer from a lack of interpretability and generalization capabilities. This paper aims to develop a family of RL-based controllers enhanced by physics-informed policies.
  arXiv  Detail & Related papers  (2024-09-23T23:02:34Z)
• Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion [66.69666636971922]
  We present APRL, a policy regularization framework that modulates the robot's exploration over the course of training. APRL enables a quadrupedal robot to efficiently learn to walk entirely in the real world within minutes.
  arXiv  Detail & Related papers  (2023-10-26T17:51:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.