Realizing Text-Driven Motion Generation on NAO Robot: A Reinforcement Learning-Optimized Control Pipeline
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
Human motion retargeting for humanoid robots, transferring human motion data to robots for imitation, presents significant challenges but offers considerable potential for real-world applications. Traditionally, this process relies on human demonstrations captured through pose estimation or motion capture systems. In this paper, we explore a text-driven approach to mapping human motion to humanoids. To address the inherent discrepancies between the generated motion representations and the kinematic constraints of humanoid robots, we propose an angle signal network based on norm-position and rotation loss (NPR Loss). It generates joint angles, which serve as inputs to a reinforcement learning-based whole-body joint motion control policy. The policy ensures tracking of the generated motions while maintaining the robot's stability during execution. Our experimental results demonstrate the efficacy of this approach, successfully transferring text-driven human motion to a real humanoid robot NAO.
Key facts. Cited by 2 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 19 of its 32 references that are in the map. First posted on arXiv 5 Jun 2025.
Builds on
19 of 32 references.
Closest ancestors · 1
Same direction, newest first
Other references · 18
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2022Human Motion Diffusion Modelcited by 1,080
- 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 780
- 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 575
- 2019Learning agile and dynamic motor skills for legged robotscited by 504
- 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 501
- 2020Action2Motion: Conditioned Generation of 3D Human Motionscited by 441
- 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
- 2022MotionCLIP: Exposing Human Motion Generation to CLIP Spacecited by 408
- 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
- 2020HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimationcited by 307
- 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
6 more
- 2023Priority-Centric Human Motion Generation in Discrete Latent Spacecited by 67
- 2017Deep Video Generation, Prediction and Completion of Human Action Sequencescited by 52
- 2019Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitionscited by 46
- 2023Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphscited by 45
- 2021Robust Feedback Motion Policy Design Using Reinforcement Learning on a 3D Digit Bipedal Robotcited by 17
- 2024Efficient Text-driven Motion Generation via Latent Consistency Trainingcited by 6
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2025RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control
- 2026TextOp: Real-time Interactive Text-Driven Humanoid Robot Motion Generation and Control
- 2025A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting
- 2024Learning from Massive Human Videos for Universal Humanoid Pose Control
- 2024I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning
- 2024Expressive Whole-Body Control for Humanoid Robots
- 2026TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation
- 2026ECHO: Edge-Cloud Humanoid Orchestration for Language-to-Motion Control