A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (1907.03146v3)

Abstract: A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.

• The paper presents a formal framework for robot manipulation by modeling tasks with Markov Decision Processes to enable learning across varied scenarios.
• It analyzes key challenges in state representation, interactive perception, and skill policy learning, providing clear strategies for addressing real-world dynamics.
• The review emphasizes hierarchical and compositional learning techniques to reduce sample complexity and improve autonomous manipulation performance.

Overview of "A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms"

The paper "A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms," authored by Oliver Kroemer, Scott Niekum, and George Konidaris, presents a comprehensive synthesis of the progress, challenges, and methodologies in robot learning for manipulation tasks. This work serves to formalize and critically analyze the field, offering a coherent framework that researchers can employ to build upon this burgeoning area of intelligent robotics.

Research Context and Scope

Robot manipulation, a core component of autonomous robotics, involves equipping robots with the ability to interact with and alter their environment to achieve designated goals. The authors recognize the inherent complexity in modeling the diverse and dynamic real-world scenarios that robots encounter. Emphasizing the imperative of learning, the paper critically reviews a subset of research that leverages machine learning to overcome these challenges, structuring the analysis around three major areas: challenges, representations, and algorithms.

Key Concepts and Structural Framework

The paper delineates several key concepts central to manipulation learning, asserting that these concepts are intrinsic to effective robotic manipulation:

1. Physical Systems and Dynamics: The authors highlight the importance of physical laws and constraints, such as underactuation and the presence of nonholonomic constraints, underscoring the need for robots to maintain mode-aware control strategies.
2. Interactive Perception: The critical role of interactive perception is outlined, where robots learn and verify environmental models through actions to adapt to unexpected scenarios and latent variables.
3. Hierarchical Task Decomposition: A hierarchical approach to handling manipulation tasks is advocated, enabling robots to decompose complex manipulations into modular, reusable, and simple subtasks or skills.

These foundational concepts form the scaffold for subsequent discussions on how robot learning systems should be designed.

Formalizing Robot Manipulation Problems

In formalizing manipulation tasks, the authors construct a task model based on Markov Decision Processes (MDPs), which integrate state, action, reward, and transition dynamics into a unified architecture. The primary extension involves defining a task family, allowing for learning across multiple tasks characterized by shared dynamics and goals but distinct environmental contexts. This formalization is vital for expanding robot capabilities to generalize learned policies across varied and unforeseen tasks.

Challenges and Methodologies

The paper categorizes the technical challenges into several sections, addressing essential aspects of robot learning:

• State Space Definition and Object Representation: Techniques for modeling objects and their interactions with robotic agents are critical for achieving accurate and adaptable manipulation skills. The paper explores both passive and interactive perception strategies and emphasizes the significance of learned hierarchical representations.
• Transition Model Learning: Effective manipulation requires a predictive understanding of how actions affect environmental states. The authors describe the learning of continuous, discrete, and hybrid transition models, elucidating how uncertainty impacts model robustness and transferability between tasks.
• Skill Policy Learning: Through an exploration of reinforcement learning and imitation learning frameworks, the paper outlines strategies for acquiring optimal control policies, while also considering exploration-exploitation trade-offs inherently tied to autonomous learning systems.
• Compositional and Hierarchical Learning: The formation of skill libraries and decision-making abstractions is positioned as a lever for reducing sample complexity and facilitating task-level planning. Constructing robust hierarchies necessitates identifying reusable skills and learning their associated conditions.

Future Directions and Implications

The implications of this research are profound, spanning practical deployment to theoretical advancements in AI. By addressing key open challenges, such as integrating learning into complete systems and improving sample efficiency, the authors identify pathways for future research. This paves the way for more versatile, resilient, and autonomous robots capable of functioning in unstructured environments with minimal human intervention.

In summary, this paper is a rich resource for researchers aiming to develop or refine approaches in robot learning for manipulation. The comprehensive analysis and formal framework it provides are invaluable for informing the design of learning algorithms and robotic architectures aimed at achieving intelligent manipulation.