Résumé
Humanoid robots hold great potential to perform various human-level skills,
involving unified locomotion and manipulation in real-world settings. Driven by
advances in machine learning and the strength of existing model-based
approaches, these capabilities have progressed rapidly, but often separately.
This survey offers a comprehensive overview of the state-of-the-art in humanoid
locomotion and manipulation (HLM), with a focus on control, planning, and
learning methods. We first review the model-based methods that have been the
backbone of humanoid robotics for the past three decades. We discuss contact
planning, motion planning, and whole-body control, highlighting the trade-offs
between model fidelity and computational efficiency. Then the focus is shifted
to examine emerging learning-based methods, with an emphasis on reinforcement
and imitation learning that enhance the robustness and versatility of
loco-manipulation skills. Furthermore, we assess the potential of integrating
foundation models with humanoid embodiments to enable the development of
generalist humanoid agents. This survey also highlights the emerging role of
tactile sensing, particularly whole-body tactile feedback, as a crucial
modality for handling contact-rich interactions. Finally, we compare the
strengths and limitations of model-based and learning-based paradigms from
multiple perspectives, such as robustness, computational efficiency,
versatility, and generalizability, and suggest potential solutions to existing
challenges.