# A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control

arXiv: https://arxiv.org/abs/2203.03391 · HTML: https://mlsanity.com/paper/2203.03391/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 42 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 28 of 28)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Large-Scale Study of Curiosity-Driven Learning](https://mlsanity.com/paper/1808.04355/) (2018)
- [Zero-Shot Visual Imitation](https://mlsanity.com/paper/1804.08606/) (2018)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Multi-expert learning of adaptive legged locomotion](https://mlsanity.com/paper/2012.05810/) (2020)
- [RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control](https://mlsanity.com/paper/2012.03094/) (2020)
- [Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic](https://mlsanity.com/paper/1901.02705/) (2019)
- [DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.08399/) (2019)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation](https://mlsanity.com/paper/2103.00946/) (2021)
- [Robust Quadruped Jumping via Deep Reinforcement Learning](https://mlsanity.com/paper/2011.07089/) (2020)
- [Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion](https://mlsanity.com/paper/2002.09676/) (2020)
- [Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control](https://mlsanity.com/paper/2011.00032/) (2020)
- [Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion](https://mlsanity.com/paper/2009.10019/) (2020)
- [From Pixels to Legs: Hierarchical Learning of Quadruped Locomotion](https://mlsanity.com/paper/2011.11722/) (2020)
- [Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space](https://mlsanity.com/paper/2102.09812/) (2021)
- [Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning](https://mlsanity.com/paper/2110.12997/) (2021)
- [Whole-Body MPC for a Dynamically Stable Mobile Manipulator](https://mlsanity.com/paper/1902.10415/) (2019)
- [Zero-Shot Terrain Generalization for Visual Locomotion Policies](https://mlsanity.com/paper/2011.05513/) (2020)
- [MPC-based Controller with Terrain Insight for Dynamic Legged Locomotion](https://mlsanity.com/paper/1909.13842/) (2019)
- [Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach](https://mlsanity.com/paper/2010.16342/) (2020)
- [CLOUD: Contrastive Learning of Unsupervised Dynamics](https://mlsanity.com/paper/2010.12488/) (2020)
- [Generating Continuous Motion and Force Plans in Real-Time for Legged Mobile Manipulation](https://mlsanity.com/paper/2104.11685/) (2021)

## Built on by (most cited first, 2 of 2)

- [QuadWBG: Generalizable Quadrupedal Whole-Body Grasping](https://mlsanity.com/paper/2411.06782/) (2024)
- [A Reactive Framework for Whole-Body Motion Planning of Mobile Manipulators Combining Reinforcement Learning and SDF-Constrained Quadratic Programmi](https://mlsanity.com/paper/2503.23975/) (2025)

## Abstract (arXiv)

Due to their ability to adapt to different terrains, quadruped robots have drawn much attention in the research field of robot learning. Legged mobile manipulation, where a quadruped robot is equipped with a robotic arm, can greatly enhance the performance of the robot in diverse manipulation tasks. Several prior works have investigated legged mobile manipulation from the viewpoint of control theory. However, modeling a unified structure for various robotic arms and quadruped robots is a challenging task. In this paper, we propose a unified framework disturbance predictive control where a reinforcement learning scheme with a latent dynamic adapter is embedded into our proposed low-level controller. Our method can adapt well to various types of robotic arms with a few random motion samples and the experimental results demonstrate the effectiveness of our method.