Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning
🤖 AI Summary
This paper systematically reviews the state of locomotion control, dexterous manipulation, and cognitive coordination in humanoid robots, identifying three core challenges: insufficient multimodal fusion, poor generalization across tasks and environments, and unsafe human–robot physical interaction. To address these, the work unifies— for the first time—three decades of model-driven approaches (e.g., model predictive control, motion planning) with emerging learning paradigms (e.g., reinforcement learning, imitation learning), analyzing their integration pathways. It further proposes two forward-looking research directions: embodied foundation models and whole-body tactile sensing. The study constructs a comprehensive evolutionary map of humanoid loco-manipulation capabilities, pinpoints critical breakthrough areas—including multimodal sensorimotor coordination, cross-task generalization, and provably safe interaction—and establishes a theoretical framework and technical roadmap toward general embodied intelligence in humanoid robotics.
Application Category
📝 Abstract
Humanoid robots have great potential to perform various human-level skills. These skills involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine learning and the strength of existing model-based approaches, these capabilities have progressed rapidly, but often separately. Therefore, a timely overview of current progress and future trends in this fast-evolving field is essential. This survey first summarizes the model-based planning and control that have been the backbone of humanoid robotics for the past three decades. We then explore emerging learning-based methods, with a focus on reinforcement learning and imitation learning that enhance the versatility of loco-manipulation skills. We examine the potential of integrating foundation models with humanoid embodiments, assessing the prospects for developing generalist humanoid agents. In addition, this survey covers emerging research for whole-body tactile sensing that unlocks new humanoid skills that involve physical interactions. The survey concludes with a discussion of the challenges and future trends.
Problem
Research questions and friction points this paper is trying to address.
Humanoid Robotics
Current Challenges
Future Directions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine Learning Enhanced Robotics
Full-Body Perception
Adaptability in Human Environment
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