FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control
cs.RO
Submitted: 2026-06-26
Updated: 2026-10-09
Comments: Project page: https://lecar-lab.github.io/FADA-humanoid/
Project page: https://lecar-lab.github.io/FADA-humanoid
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization
- Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection
- Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain
- Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation
- A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
- HumanPlus: Humanoid Shadowing and Imitation from Humans
- OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
- HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots
- ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills
- Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids
- BarlowWalk: Self-supervised Representation Learning for Legged Robot Terrain-adaptive Locomotion
- Towards Bridging the Gap between Large-Scale Pretraining and Efficient Finetuning for Humanoid Control
- TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
- Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding
- DiffCoTune: Differentiable Co-Tuning for Cross-domain Robot Control
- RMA: Rapid Motor Adaptation for Legged Robots
- Adapting Rapid Motor Adaptation for Bipedal Robots
- Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments
- Sym2Real: Symbolic Dynamics with Residual Learning for Data-Efficient Adaptive Control
- Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
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