Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning (2501.02116v2)

Abstract: 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.

• The paper presents a comprehensive survey that integrates model-based control with learning-based approaches to enhance humanoid robotics.
• It evaluates both optimization-based and closed-form whole-body control techniques for achieving real-time locomotion and precise manipulation.
• The integration of reinforcement and imitation learning is highlighted as a crucial strategy for overcoming sim-to-real transfer challenges.

Insightful Overview of Humanoid Locomotion and Manipulation: Current Progress and Challenges

The paper "Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning" presents a comprehensive survey of the recent advancements in the field of humanoid robotics, emphasizing the confluence of traditional model-based techniques and modern learning-based approaches. This survey unifies the dimensions of control, planning, and learning, constituting the core aspects necessary for the development of versatile humanoid robots capable of executing complex loco-manipulation tasks.

Focused Summary of Methodologies and Challenges

A central theme in the survey is the integration of model-based control methods, which have dominated the domain for decades, with emerging learning-based approaches. Model Predictive Control (MPC), particularly in its various forms such as Nonlinear MPC and Linear Convex MPC, remains a fundamental technique for locomotion planning due to its ability to handle complex dynamic models and constraints. However, the paper highlights the computational limitations that often necessitate the use of simplified dynamics, such as the Linear Inverted Pendulum Model or the Single Rigid Body Model, to achieve real-time performance.

The survey also explores whole-body control strategies, where both closed-form and optimization-based methods are scrutinized for their ability to achieve task-space control and enforce task hierarchies. Optimization-based controllers, especially those formulated as quadratic programs, are praised for their versatility and robustness, albeit at the cost of higher computational demand.

On the learning front, Reinforcement Learning (RL) and Imitation Learning (IL) are showcased as potent tools for enhancing the skill repertoire of humanoid robots. RL's capacity for discovering novel behaviors through interaction is balanced by IL's efficiency in acquiring skills from demonstrations, providing a dual pathway to skill generalization.

Notable Claims and Results

The survey outlines bold claims regarding the potential of integrating reinforcement learning with imitation learning for faster adaptation and the ability to execute complex multi-contact tasks. Such integration could ostensibly address the persistent challenge of sim-to-real transfer, a notable bottleneck in deploying learning-based policies on hardware.

Implications and Future Directions

Practically, the paper implies that advancements in tactile sensing, especially whole-body tactile capabilities, could revolutionize humanoid interaction by providing rich contact feedback, crucial for manipulation tasks in unstructured environments. Theoretically, the emergence of Foundation Models (FMs) in robotics suggests a paradigm shift, where pre-trained models on vast datasets could imbue humanoid robots with semantic understanding and decision-making capabilities akin to generalist agents.

The paper speculates that the development of humanoid robots, endowed with generalization capabilities from foundation models and enhanced sensorimotor skills from integrated learning methods, could lead to significant breakthroughs in humanoid applications, ranging from manufacturing to human-robot collaboration.

Conclusion

In summary, this paper effectively synthesizes the state-of-the-art in humanoid robotics, balancing traditional control strategies with contemporary learning techniques. It foresees a future where humanoid robots seamlessly integrate complex planning and control with learning models, paving the way for robots that are not only adaptable and capable but also generally intelligent. The survey sets the stage for ongoing research to address the computational challenges and data scarcity issues, ultimately fostering the deployment of sophisticated humanoid robots in real-world situations.