Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (2408.03539v3)

Abstract: Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated robotic behaviors. Robotics problems, however, pose fundamental difficulties for the application of RL, stemming from the complexity and cost of interacting with the physical world. This article provides a modern survey of DRL for robotics, with a particular focus on evaluating the real-world successes achieved with DRL in realizing several key robotic competencies. Our analysis aims to identify the key factors underlying those exciting successes, reveal underexplored areas, and provide an overall characterization of the status of DRL in robotics. We highlight several important avenues for future work, emphasizing the need for stable and sample-efficient real-world RL paradigms, holistic approaches for discovering and integrating various competencies to tackle complex long-horizon, open-world tasks, and principled development and evaluation procedures. This survey is designed to offer insights for both RL practitioners and roboticists toward harnessing RL's power to create generally capable real-world robotic systems.

• The paper surveys how deep reinforcement learning is applied to real-world robotics, detailing successes in locomotion, navigation, and manipulation.
• It emphasizes innovative methodologies like sim-to-real transfer, domain randomization, and hierarchical policy integration to overcome physical deployment challenges.
• The survey identifies underexplored areas and outlines future research directions to advance robust, sample-efficient, and safe DRL algorithms in robotics.

Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Overview

The paper "Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes," authored by Tang et al., provides an extensive survey of the use of Deep Reinforcement Learning (DRL) in the field of robotics. DRL has demonstrated significant potential for enabling sophisticated robotic behaviors, yet deploying DRL in robotic systems operating in the physical world comes with unique challenges. This survey examines the real-world successes of DRL in various robotic domains and identifies key factors contributing to these successes, uncovers underexplored areas, and highlights future research directions.

Context and Motivation

Reinforcement Learning (RL) involves learning to make sequential decisions by interacting with an environment to maximize cumulative rewards. When combined with deep learning, forming Deep RL (DRL), these techniques have achieved remarkable feats in areas such as board games, video games, healthcare, and recommendation systems. However, while most DRL successes are confined to simulated environments, applying DRL to robotics involves additional complexity due to the interaction with the physical world, which can be both costly and unsafe.

The motivation behind this survey stems from the necessity to bridge the gap between DRL's impressive results in simulations and its application to real-world robotic systems. This paper aims to evaluate current progress in real-world DRL for robotics, identify key enablers and challenges, and provide a comprehensive assessment across various robotic competencies and application domains.

Taxonomy

The paper introduces a novel taxonomy to systematically categorize and assess the DRL literature:

1. Robot Competencies:
   • Single-Robot Competencies: Mobility (locomotion and navigation) and manipulation (stationary and mobile).
   • Multi-Agent Competencies: Human-robot interaction and multi-robot interaction.
2. Problem Formulation: This axis involves characterizing an RL problem based on its action space (low, mid, or high-level), observation space (high-dimensional sensor inputs or low-dimensional state vectors), and reward function (sparse or dense).
3. Solution Approach: This includes various elements such as simulator usage (zero-shot or few-shot sim-to-real, or no simulation), model learning (learning dynamics models), expert usage (human demonstrations or oracle policies), policy optimization algorithm (planning, offline, on-policy, or off-policy learning), and the type of policy/model representation adopted (MLP, CNN, RNN, or Transformer networks).
4. Level of Real-World Success: A novel metric introduced by the authors to assess the maturity of DRL solutions in real-world applications, categorized into six levels from simulation-only to commercial deployment.

Key Insights and Findings

Locomotion

• Quadruped Locomotion: DRL has matured to enable robust and adaptive quadruped locomotion across diverse terrains and environments. Key strategies involve sim-to-real transfer, often coupled with extensive domain randomization and hierarchical policies interfacing with model-based controllers. Successful examples include zero-shot sim-to-real deployments in production-level systems like those of ANYbotics and Boston Dynamics.
• Biped Locomotion: Achievements in biped locomotion are less extensive due to the complex, under-actuated dynamics of bipeds. While there are impressive demonstrations in controlled environments, full deployment across variable terrains remains challenging. Techniques such as privileged learning and sequence models have shown promise.
• Quadrotor Control: DRL has demonstrated robust, agile flight control for quadrotors in controlled indoor environments, leveraging techniques like domain randomization and learned residual models to improve sim-to-real transfer.

Navigation

• Wheeled, Legged, and Aerial Navigation: Navigation tasks often involve modular designs where DRL components replace specific parts of a classical navigation stack. However, complete end-to-end DRL solutions still face challenges in terms of robustness and interpretability. Visual navigation has seen notable advancements with modular approaches combining mapping and planning.
• Autonomous Driving: Despite limited success in real-world field tests, RL-based techniques showcase potential for improving specific modules, such as local planners or adaptive cruising policies.

Manipulation

• Pick-and-Place and Grasping: DRL has shown efficacy in solving grasping subproblems using classification or bandit formulations. For end-to-end pick-and-place tasks, the challenge remains to scale DRL to handle diverse, open-world environments. Successful instances often include hybrid approaches, combining sim-to-real transfer with data-efficient learning algorithms.
• Contact-Rich and Non-Prehensile Manipulation: DRL has tackled tasks like object assembly and deformable object manipulation with notable success using strategies such as residual RL and extensive domain randomization. However, real-world deployments are still limited due to high dynamic complexities.
• In-Hand Manipulation: Recent advancements in vision and tactile sensing, combined with DRL, have enabled complex in-hand manipulation tasks, though integrating these skills with other manipulation strategies (e.g., tool use) remains an open problem.

Multi-Robot Interaction and HRI

• Human-Robot Interaction (HRI): HRI tasks pose unique safety and interpretability challenges. DRL applications in collaborative and non-collaborative pHRI, particularly social navigation, have achieved initial success, though scaling these solutions to more complex, real-world tasks remains ongoing.
• Multi-Robot Interaction: Multi-robot RL solutions are mostly confined to cooperative settings such as robot soccer and collision avoidance. These solutions underscore the scalability challenges and complexities in interactions as the number of robots increases.

Implications and Future Directions

Practical and Theoretical Implications

1. Real-World Learning: Efficient and safe real-world learning procedures are paramount. Techniques like automatic resets, safe exploration mechanisms, and leveraging real-world adaptation could push the boundaries of DRL in tasks where high-fidelity simulation is unattainable.
2. Long-Horizon Tasks: Combining learned skills to address long-horizon tasks is crucial. Approaches like hierarchical RL, end-to-end training, and planning within skills show promise and need further development.
3. Stable and Sample-Efficient Algorithms: Enhancing the stability and sample efficiency of RL algorithms is vital, particularly for off-policy and offline methods, to reduce the reliance on extensive simulation data and facilitate real-world applicability.
4. Principled Approaches: Developing a principled methodology for RL system design, including action space definitions, reward structures, and incorporating classical control with learning-based approaches, is critical for creating deployable solutions.

Speculative Future Developments

• Foundation Models: Leveraging large-scale robotic datasets and foundation models presents exciting opportunities to address DRL's generalization challenges and achieve better representations and policies across diverse tasks.
• Robot Deployments: The gradual improvement of RL algorithms and their integration with foundational models may lead to versatile, autonomous robots capable of performing complex tasks in dynamic and unstructured environments.

Conclusion

In sum, DRL is significantly shaping the future of robotics, evidenced by numerous real-world successes. This survey highlights substantial progress across various robotic applications while emphasizing the need for advances in algorithm efficiency, safe real-world learning, and principled system design to further expand DRL's applicability and improve the reliability of robotic systems.