# Learning More With Less: Sample Efficient Model-Based RL for Loco-Manipulation

arXiv: https://arxiv.org/abs/2501.10499 · HTML: https://mlsanity.com/paper/2501.10499/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2025.

## Built on by (most cited first, 1 of 1)

- [Neural Robot Dynamics](https://mlsanity.com/paper/2508.15755/) (2025)

## Abstract (arXiv)

By combining the agility of legged locomotion with the capabilities of manipulation, loco-manipulation platforms have the potential to perform complex tasks in real-world applications. To this end, state-of-the-art quadrupeds with manipulators, such as the Boston Dynamics Spot, have emerged to provide a capable and robust platform. However, the complexity of loco-manipulation control, as well as the black-box nature of commercial platforms, pose challenges for deriving accurate dynamics models and robust control policies. To address these challenges, we turn to model-based reinforcement learning (RL). We develop a hand-crafted kinematic model of a quadruped-with-arm platform which - employing recent advances in Bayesian Neural Network (BNN)-based learning - we use as a physical prior to efficiently learn an accurate dynamics model from limited data. We then leverage our learned model to derive control policies for loco-manipulation via RL. We demonstrate the effectiveness of our approach on state-of-the-art hardware using the Boston Dynamics Spot, accurately performing dynamic end-effector trajectory tracking even in low data regimes. Project website and videos: https://sites.google.com/view/learning-more-with-less.