mlsanityData to 14 Oct 2026

Latent Conditioned Loco-Manipulation Using Motion Priors

arXiv 2509.16061 ↗First posted 19 Sep 2025cs.ROcited by 3

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Although humanoid and quadruped robots provide a wide range of capabilities, current control methods, such as Deep Reinforcement Learning, focus mainly on single skills. This approach is inefficient for solving more complicated tasks where high-level goals, physical robot limitations and desired motion style might all need to be taken into account. A more effective approach is to first train a multipurpose motion policy that acquires low-level skills through imitation, while providing latent space control over skill execution. Then, this policy can be used to efficiently solve downstream tasks. This method has already been successful for controlling characters in computer graphics. In this work, we apply the approach to humanoid and quadrupedal loco-manipulation by imitating either simple synthetic motions or kinematically retargeted dog motions. We extend the original formulation to handle constraints, ensuring deployment safety, and use a diffusion discriminator for better imitation quality. We verify our methods by performing loco-manipulation in simulation for the H1 humanoid and Solo12 quadruped, as well as deploying policies on Solo12 hardware. Videos and code are available at https://gepetto.github.io/LaCoLoco/

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 23 references that are in the map. First posted on arXiv 19 Sep 2025.

Builds on

18 of 23 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes7 Aug
  2. 2024CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning27 Mar
  3. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control30 Jan

Other references · 15

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2016Generative Adversarial Imitation Learningcited by 1,597
  4. 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 752
  5. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  6. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
  7. 2024HOVER: Versatile Neural Whole-Body Controller for Humanoid Robotscited by 91
  8. 2022Adversarial Motion Priors Make Good Substitutes for Complex Reward Functionscited by 89
  9. 2025Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learningcited by 61
  10. 2023Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotioncited by 39
  11. 2024Pedipulate: Enabling Manipulation Skills using a Quadruped Robot's Legcited by 27
  12. 2019An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Researchcited by 22
3 more
  1. 2022Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motionscited by 21
  2. 2024Learning Visual Quadrupedal Loco-Manipulation from Demonstrationscited by 20
  3. 2024Diffusion-Reward Adversarial Imitation Learningcited by 20

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Direct Dynamic Retargeting for Humanoid Imitation Learning from Videoscited by 2
  2. 2026LatentMimic: Terrain-Adaptive Locomotion via Latent Space Imitationcited by 0
  3. 2026Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning
  2. 2026FetchMan: Learning Visual Humanoid Loco-Manipulation Policies from Simulated Experiences
  3. 2025Learning a Unified Policy for Position and Force Control in Legged Loco-Manipulation
  4. 2023Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation
  5. 2024Data-Efficient Approach to Humanoid Control via Fine-Tuning a Pre-Trained GPT on Action Data
  6. 2024Latent Action Priors for Locomotion with Deep Reinforcement Learning
  7. 2025Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots
  8. 2022Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning