Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges (1907.00182v3)

Abstract: Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective changes through time, or where all the training data and objective criteria are never available at once. The evolution of the learning process is modeled by a sequence of learning experiences where the goal is to be able to learn new skills all along the sequence without forgetting what has been previously learned. Continual learning also aims at the same time at optimizing the memory, the computation power and the speed during the learning process. An important challenge for machine learning is not necessarily finding solutions that work in the real world but rather finding stable algorithms that can learn in real world. Hence, the ideal approach would be tackling the real world in a embodied platform: an autonomous agent. Continual learning would then be effective in an autonomous agent or robot, which would learn autonomously through time about the external world, and incrementally develop a set of complex skills and knowledge. Robotic agents have to learn to adapt and interact with their environment using a continuous stream of observations. Some recent approaches aim at tackling continual learning for robotics, but most papers on continual learning only experiment approaches in simulation or with static datasets. Unfortunately, the evaluation of those algorithms does not provide insights on whether their solutions may help continual learning in the context of robotics. This paper aims at reviewing the existing state of the art of continual learning, summarizing existing benchmarks and metrics, and proposing a framework for presenting and evaluating both robotics and non robotics approaches in a way that makes transfer between both fields easier.

Continual Learning for Robotics: Critical Insights and Framework

The paper presented explores the current paradigms and challenges of Continual Learning (CL) within the context of robotics. CL addresses scenarios where data distributions and learning objectives evolve over time, posing the problem of retaining acquired knowledge without succumbing to catastrophic forgetting. This is particularly applicable in robotics, where agents interact with dynamic real-world environments.

Key Concepts and Framework

CL is integral to robotics, enabling autonomous agents to learn and adapt without needing to retrain from scratch. The paper discusses the need for a robust framework to define and evaluate CL algorithms, emphasizing the dynamic nature of data streams and the temporal constraints inherent to robotic systems. The proposed framework categorizes CL scenarios into Single-Incremental-Task (SIT), Multi-Task (MT), and Multi-Incremental-Task (MIT). These scenarios define how tasks are presented over time and influence algorithm design and evaluation.

Learning Strategies

The review of CL strategies covers:

• Dynamic Architectures: These involve explicit or implicit modifications to model architectures to integrate new knowledge while preserving previously acquired skills. Dual-memory systems, inspired by the interaction between the hippocampus and neocortex, offer a promising approach.
• Regularization Techniques: These focus on preventing forgetting by regulating the learning process. Methods range from penalty-based regularization like Elastic Weight Consolidation (EWC) to knowledge distillation, moderating updates to critical parameters.
• Rehearsal and Generative Replay: These strategies involve retaining or generating past data to facilitate knowledge retention. Generative models, especially GANs, offer a sophisticated solution by re-creating prior experiences.

Evaluation and Benchmarks

The paper underscores the importance of comprehensive metrics and benchmarks to assess CL algorithms, with metrics such as Average Accuracy (ACC), Backward Transfer (BWT), and Forward Transfer (FWT) being crucial for evaluating learning efficiency and memory retention. It highlights the limitation of traditional datasets like MNIST for CL evaluation and advocates for more complex and realistic scenarios, particularly those involving robotics.

Challenges and Applications in Robotics

Robotics provides a pertinent application domain for CL due to its inherently dynamic and resource-limited nature. Challenges in applying CL to robotics include handling real-time data acquisition, stability during learning, managing memory and computational constraints, and dealing with novel and unforeseen environmental changes. The continual learning capabilities enable robots not just to adapt post-deployment but to refine models over time with minimal human intervention.

Implications and Future Directions

The implications of robust CL for robotics are vast, suggesting potential in diverse fields such as autonomous vehicles, real-time adaptation in changing environments, and in-the-wild object recognition. Future research is likely to focus on improving the scalability and efficiency of CL algorithms and extending their applicability to more complex robotic systems and tasks.

In conclusion, the paper provides a rich landscape for advancing CL in robotics, offering a comprehensive framework, diverse strategic approaches, and critical insights into evaluation and applications. The integration of CL with robotics holds promise for transformative advancements in creating intelligent, adaptive, and autonomous systems capable of lifelong learning.