# Learning Visual Quadrupedal Loco-Manipulation from Demonstrations

arXiv: https://arxiv.org/abs/2403.20328 · HTML: https://mlsanity.com/paper/2403.20328/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 46 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 30 of 37)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning](https://mlsanity.com/paper/2107.09645/) (2021)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments](https://mlsanity.com/paper/2108.03332/) (2021)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Legged Locomotion in Challenging Terrains using Egocentric Vision](https://mlsanity.com/paper/2211.07638/) (2022)
- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning](https://mlsanity.com/paper/2206.08686/) (2022)
- [3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations](https://mlsanity.com/paper/2403.03954/) (2024)
- [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023)
- [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://mlsanity.com/paper/2202.05481/) (2022)
- [Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World](https://mlsanity.com/paper/2110.05457/) (2021)
- [DTC: Deep Tracking Control](https://mlsanity.com/paper/2309.15462/) (2023)
- [Advanced Skills by Learning Locomotion and Local Navigation End-to-End](https://mlsanity.com/paper/2209.12827/) (2022)
- [ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation](https://mlsanity.com/paper/2304.00410/) (2023)
- [DribbleBot: Dynamic Legged Manipulation in the Wild](https://mlsanity.com/paper/2304.01159/) (2023)
- [Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion](https://mlsanity.com/paper/2303.11330/) (2023)
- [Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot](https://mlsanity.com/paper/2208.01160/) (2022)
- [Perceptive Locomotion through Nonlinear Model Predictive Control](https://mlsanity.com/paper/2208.08373/) (2022)
- [Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning](https://mlsanity.com/paper/2203.14912/) (2022)
- [Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation](https://mlsanity.com/paper/2308.09179/) (2023)
- [Adaptive Mobile Manipulation for Articulated Objects In the Open World](https://mlsanity.com/paper/2401.14403/) (2024)
- [Pedipulate: Enabling Manipulation Skills using a Quadruped Robot's Leg](https://mlsanity.com/paper/2402.10837/) (2024)

## Built on by (most cited first, 20 of 20)

- [TWIST: Teleoperated Whole-Body Imitation System](https://mlsanity.com/paper/2505.02833/) (2025)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [Failure-Aware RL: Reliable Offline-to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation](https://mlsanity.com/paper/2601.07821/) (2026)
- [QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning](https://mlsanity.com/paper/2412.15576/) (2024)
- [Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining](https://mlsanity.com/paper/2506.16475/) (2025)
- [Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models](https://mlsanity.com/paper/2410.00231/) (2024)
- [Unsupervised Skill Discovery as Exploration for Learning Agile Locomotion](https://mlsanity.com/paper/2508.08982/) (2025)
- [Versatile Loco-Manipulation through Flexible Interlimb Coordination](https://mlsanity.com/paper/2506.07876/) (2025)
- [Combining Planning and Diffusion for Mobility with Unknown Dynamics](https://mlsanity.com/paper/2410.06911/) (2024)
- [Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams](https://mlsanity.com/paper/2505.13834/) (2025)
- [Bipedalism for Quadrupedal Robots: Versatile Loco-Manipulation through Risk-Adaptive Reinforcement Learning](https://mlsanity.com/paper/2507.20382/) (2025)
- [Latent Conditioned Loco-Manipulation Using Motion Priors](https://mlsanity.com/paper/2509.16061/) (2025)
- [Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation](https://mlsanity.com/paper/2601.02778/) (2026)
- [SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning](https://mlsanity.com/paper/2502.12674/) (2025)
- [GPO: Growing Policy Optimization for Legged Robot Locomotion and Whole-Body Control](https://mlsanity.com/paper/2601.20668/) (2026)
- [CAIMAN: Causal Action Influence Detection for Sample-efficient Loco-manipulation](https://mlsanity.com/paper/2502.00835/) (2025)
- [Dynamic Legged Ball Manipulation on Rugged Terrains with Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2504.14989/) (2025)
- [Scoop-and-Toss: Dynamic Object Collection for Quadrupedal Systems](https://mlsanity.com/paper/2506.09406/) (2025)
- [Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation](https://mlsanity.com/paper/2606.07506/) (2026)
- [Learning to Stack: Cube-Stacking Imitation Learning from Virtual Reality Demonstrations](https://mlsanity.com/paper/2609.19040/) (2026)

## Abstract (arXiv)

Quadruped robots are progressively being integrated into human environments. Despite the growing locomotion capabilities of quadrupedal robots, their interaction with objects in realistic scenes is still limited. While additional robotic arms on quadrupedal robots enable manipulating objects, they are sometimes redundant given that a quadruped robot is essentially a mobile unit equipped with four limbs, each possessing 3 degrees of freedom (DoFs). Hence, we aim to empower a quadruped robot to execute real-world manipulation tasks using only its legs. We decompose the loco-manipulation process into a low-level reinforcement learning (RL)-based controller and a high-level Behavior Cloning (BC)-based planner. By parameterizing the manipulation trajectory, we synchronize the efforts of the upper and lower layers, thereby leveraging the advantages of both RL and BC. Our approach is validated through simulations and real-world experiments, demonstrating the robot's ability to perform tasks that demand mobility and high precision, such as lifting a basket from the ground while moving, closing a dishwasher, pressing a button, and pushing a door. Project website: https://zhengmaohe.github.io/leg-manip