2026-07-30 04:00 UTCOriginal source2 min readUpdated: 2026-07-30 08:44 UTC

Reinforcement Learning on Cost-Constrained Quadrupedal Hardware

Deploying learned control policies on low-cost robotic platforms (e.g., Mini Pupper 2) introduces transport latencies >50 ms that widen the sim-to-real gap. By using a forward model of average actuator delay and a time-aware neural network, researchers achieved robust locomotion and observed the emergence of a central pattern generator (CPG) that withstands +320 ms latency perturbations. The work suggests temporal self-organization as a general strategy for cost-constrained locomotion.

SourcearXiv RoboticsAuthor: Javier C. Weddington, Bence P. \"Olveczky, Stephen A. Baccus

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[Submitted on 29 Jul 2026]

Title:Reinforcement Learning on Cost-Constrained Quadrupedal Hardware

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Abstract: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.

Comments: Sim-to-real transfer, locomotion, reinforcement learning, central pattern generator

Subjects:

Robotics (cs.RO); Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.26434 [cs.RO]

(or arXiv:2607.26434v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.26434

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Javier Weddington [view email] [v1] Wed, 29 Jul 2026 03:22:12 UTC (5,630 KB)

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