# Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots

arXiv: https://arxiv.org/abs/2509.06342 · HTML: https://mlsanity.com/paper/2509.06342/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2025.

## Built on by (most cited first, 12 of 12)

- [Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learning](https://mlsanity.com/paper/2603.15789/) (2026)
- [Olaf: Bringing an Animated Character to Life in the Physical World](https://mlsanity.com/paper/2512.16705/) (2025)
- [ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation](https://mlsanity.com/paper/2604.11138/) (2026)
- [Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models](https://mlsanity.com/paper/2607.04546/) (2026)
- [HALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable Simulation](https://mlsanity.com/paper/2603.15084/) (2026)
- [Fauna Sprout: A lightweight, approachable, developer-ready humanoid robot](https://mlsanity.com/paper/2601.18963/) (2026)
- [CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion](https://mlsanity.com/paper/2606.19633/) (2026)
- [Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decomposition](https://mlsanity.com/paper/2608.09762/) (2026)
- [DeepInsight II: One Trace from Benchmark to Robot](https://mlsanity.com/paper/2608.16556/) (2026)
- [Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation](https://mlsanity.com/paper/2609.21307/) (2026)
- [Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots](https://mlsanity.com/paper/2610.08812/) (2026)
- [A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control](https://mlsanity.com/paper/2610.12465/) (2026)

## Abstract (arXiv)

Legged robots must achieve both robust locomotion and energy efficiency to be practical in real-world environments. Yet controllers trained in simulation often fail to transfer reliably, and most existing approaches neglect actuator-specific energy losses or depend on complex, hand-tuned reward formulations. We propose a framework that integrates sim-to-real reinforcement learning with a physics-grounded energy model for permanent magnet synchronous motors. The framework requires a minimal parameter set to capture the simulation-to-reality gap and employs a compact four-term reward with a first-principle-based energetic loss formulation that balances electrical and mechanical dissipation. We evaluate and validate the approach through a bottom-up dynamic parameter identification study, spanning actuators, full-robot in-air trajectories and on-ground locomotion. The framework is tested on three primary platforms and deployed on ten additional robots, demonstrating reliable policy transfer without randomization of dynamic parameters. Our method improves energetic efficiency over state-of-the-art methods, achieving a 32 percent reduction in the full Cost of Transport of ANYmal (value 1.27). All code, models, and datasets are publicly available.