mlsanityData to 14 Oct 2026

Reinforcement Learning on Cost-Constrained Quadrupedal Hardware

arXiv 2607.26434 ↗First posted 29 Jul 2026cs.RO cs.AIcited by 0

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

Abstract (arXiv)

Deploying learned control policies on low-cost robotic platforms introduces transport latencies and noisy motor feedback that systematically widens the sim-to-real gap. The chasm of simulation to deployment in hardware lies in the delay of the actuator reaching the commanded position. On platforms such as the Mini Pupper 2, a measured >50 ms transport delay transforms the locomotion task from a standard Markov decision process into a partially observable one. In this paper, we take a biologically inspired approach of handling noisy and delayed feedback to close the sim-to-real gap, thereby expanding the capability of reinforcement learning on cost-constrained hardware. Using a low-cost quadrupedal hardware platform, we find that using a forward model of the average actuator delay, paired with a time-aware neural network results in robust locomotion. Additionally, our time-aware neural network learned a central pattern generator (CPG): a self-sustaining rhythmic gait that is robust to +320 ms latency perturbations, mirroring the CPGs found in the spinal cords of vertebrates. We posit that temporal self-organization may be a general strategy for cost-constrained locomotion.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 21 references that are in the map. First posted on arXiv 29 Jul 2026.

Builds on

11 of 21 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  3. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  4. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  5. 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
  6. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
  7. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  8. 2020Reinforcement Learning with Random Delayscited by 59
  9. 2023DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Controlcited by 7
  10. 2024Attractor-based models for sequences and pattern generation in neural circuitscited by 1
  11. 2025Ground contact and reaction force sensing for linear policy control of quadruped robotcited by 1

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  1. 2024Learning to Control Emulated Muscles in Real Robots: Towards Exploiting Bio-Inspired Actuator Morphology ↗
  2. 2026Learning Fault-Tolerant Locomotion with Adaptive Gait Timing
  3. 2025Coordinating Spinal and Limb Dynamics for Enhanced Sprawling Robot Mobility
  4. 2024Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion
  5. 2026Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications
  6. 2025Reference Free Platform Adaptive Locomotion for Quadrupedal Robots using a Dynamics Conditioned Policy
  7. 2026Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain
  8. 2026Learning Quadruped Walking from Seconds of Demonstration