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Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges
Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, Natalia Díaz-Rodríguez
TL;DR
Continual learning must acquire skills from changing, incomplete data streams without forgetting earlier knowledge, a challenge especially relevant to embodied robots. This paper reviews the field, proposes a framework and evaluation resources linking robotics with non-robotics approaches, and identifies practical evaluation boundaries including robotics hardware and real-data requirements.
Problem
Existing continual-learning evaluations often use simulation or static datasets, providing limited insight into applicability to robotics, where agents learn from continuous observations.
Method
The paper reviews continual-learning research and proposes a generalized framework, benchmarks, metrics, and guidelines for presenting and evaluating robotics and non-robotics approaches.
Results
The paper organizes continual-learning strategies, evaluation techniques, and robotics-specific considerations to facilitate comparison and transfer between machine learning and robotics.
Takeaways & Limitations
The framework and gathered metrics can help categorize approaches and articulate evaluation dimensions for embodied agents that learn continually.
Takeaways & Limitations
Robotics experiments are constrained by fragile, costly hardware, failures, manual recovery, and difficulties sustaining long experiments without recharging.
Abstract
from arXiv · showhide
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 recent 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.
1 Introduction
Continual learning addresses changing data and objectives by learning cumulative skills over time while retaining prior knowledge. The paper connects this challenge to robotics and proposes a framework, terminology, metrics, and transfer-oriented presentation across fields.
- Continual learning handles data distributions that change over time and knowledge fusion across never-ending data streams.It targets catastrophic forgetting while learning under nonstationary conditions.
- Tasks are temporally bounded subsections of a data stream whose distributions or objectives may remain stationary during each period.Tasks may be disjoint or related according to their learning objectives.
- Continual learning is necessary when original data is unavailable, storage or computational power is limited, or task changes are unknown.These constraints prevent simply retraining conventionally on all shuffled data.
- Online representations must retain important knowledge while forgetting information that is unlikely to matter for future learning.Data may be discarded or have a limited lifetime.
- The paper proposes a robotics-linked framework that formalizes continual learning approaches and supports systematic transfer between robotics and non-robotics fields.It also introduces metrics and discusses robotics-specific opportunities.
2 Definition of Continual Learning
The paper defines continual learning as learning from sequential partial experiences when all data is not simultaneously available. It distinguishes data, model, and conceptual knowledge fusion while relating CL to incremental, lifelong, and never-ending learning.
- Continual learning processes a potentially unlimited sequence of partial experiences rather than accessing all data at once.It may involve scarce or imbalanced data, distribution shifts, and catastrophic forgetting.
- CL requires data-level fusion while preserving knowledge from data that may disappear or become inaccessible.Examples include reprocessing constraints, data-erasure rights, and legacy records.
- Model-level fusion combines architecture components needed by different tasks so they ultimately act as one model.
- Knowledge-level fusion avoids raw-data memorization while seeking to prevent catastrophic forgetting.
- The paper treats continual learning as synonymous with incremental, lifelong, and never-ending learning.
2.1 History and Motivation
Continual learning gained systematic attention only recently, as earlier machine learning faced limited data, computation, handcrafted features, and supervised-learning constraints. Robotics provides a key embodied setting for applying and further understanding lifelong learning.
- Continual learning existed conceptually in AI and robotics but became systematic in machine learning around the late 20th century.Lifelong learning was popularized around 1995, while robotics later renewed interest through developmental robotics.
- Earlier research lacked systemic approaches for complex, dynamic tasks combining multiple components and learning algorithms.This also complicated training, evaluation, and comparison with static-learning performance.
- Limited data and computational power hindered complex continual-learning solutions involving multiple tasks and temporal structure.
- Handcrafted features and task-specific solutions limited general algorithms and required new tasks to reuse or redesign features.
- The historical focus on supervised learning made continuous learning impractical because labeled data is slow and expensive to create.
- Recent machine learning and technological advances opened the door to more complex continual-learning problems, including embodied robotics.Robotics also raises questions about embodiment in intelligent machines that learn and think like humans.
2.2 Terminology Clarification
The paper distinguishes continual learning from related paradigms by their data-update procedures, task sequencing, objectives, transfer goals, and interaction with labeling sources.
- Online learning: Online learning is a special case of continual learning that updates after each individual data point with batch size one.It suits settings requiring instant processing or where data cannot be saved.
- Few-shot Learning: Few-shot learning recognizes new concepts from few samples, while zero-shot learning detects new classes without training examples from those classes.Few-shot learning can be used when continual-learning data is very scarce.
- Curriculum Learning: Curriculum learning orders increasingly difficult tasks so the sequence enables learning a final harder task.Its task sequence is designed around the final objective.
- Meta-learning: Meta-learning uses information from past experiences to improve learning efficiency on new experiences, not necessarily to preserve previous-task performance.It may or may not be used within continual learning.
- Transfer learning: Transfer learning applies knowledge from a previous task to a new task without requiring retention of the ability to solve the previous task.In computer vision, transfer across environments is often called domain adaptation.
- Active Learning: Active learning interactively queries labels and can control which examples a continual-learning algorithm receives.
2.3 Challenges Addressed by CL
Continual learning addresses nonstationary data streams by tackling catastrophic forgetting, memory management, and distribution shifts that may require autonomous detection and adaptation.
- Catastrophic forgetting is performance degradation on previously learned concepts when a model is trained sequentially on new concepts.
- Continual learning strategies must preserve memories of past tasks, which may be stored as raw data, representations, weights, or regularization matrices.
- Assessing memory degradation is difficult because past data and labels may no longer be available for evaluating what has been forgotten.
- When the data distribution shifts, models must detect the change and adjust without external information; otherwise, forgetting can result.
- Concept drift denotes changes in the data distribution over time and includes virtual and real forms of drift.
2.4 Learning Paradigms Orthogonal to Continual Learning
Continual learning intersects supervised, unsupervised, and reinforcement learning, but robotics often lacks the labels or rewards needed to apply conventional methods directly.
- Supervised learning: Supervised continual learning learns a function from a sequence of data points when the full dataset is not available simultaneously.
- Supervised learning: In robotics, limited supervision often prevents direct application of supervised continual-learning methods.
- Unsupervised learning: Unsupervised continual learning can build robust multimodal representations over time for later fine-tuning with sparse environmental feedback.
- Unsupervised learning: Continual image generation requires models to reproduce images from distributions that change over time and retain the whole distribution.
- Unsupervised learning: Generated data can support continual classification, reinforcement learning, and data augmentation.
- Reinforcement learning: Reinforcement learning trains agents to maximize expected cumulative reward, and complex environments can often be framed as continual-learning situations.
- Reinforcement learning: RL uses continual-learning-related tools such as parallel actors, replay buffers, and constrained updates, while many CL-RL studies remain simulation-based.
3 A Framework for Continual Learning
The proposed framework formalizes continual learning as sequential learning over changing distributions and standardizes descriptions of data, algorithms, assumptions, resources, supervision, and task structure.
- The framework responds to limited common formalization by supporting clearer evaluation, reduced ambiguity, and fairer comparisons across continual-learning algorithms.
- It asks researchers to specify data storage, tuning procedures, distribution assumptions, task recurrence, pretrained models, memory, computation, data types, and supervision.
- The framework requires descriptions of the data stream, its use, algorithm functioning, prior knowledge, and memory and computation requirements.
- The data stream is a potentially infinite sequence of unknown distributions, with each timestep providing one or more observations in a training set.
- Tasks are learning experiences identified by labels, but task boundaries need not correspond bijectively to data distributions.
- Continual learning is modeled as an algorithm that maps the previous hypothesis, current training set, previous memory, and task label to an updated hypothesis and memory.
- The framework distinguishes task scenarios including multi-task, multi-incremental-task, and single-incremental-task settings, where concept drift can occur within one task.
- Online processing is especially relevant to robotics because it can reduce adaptation time and operational memory usage in embedded settings.
4 Continual Learning Strategies
Continual-learning strategies modify model architecture or parameter usage to learn new concepts while limiting interference with prior knowledge, often trading capacity or flexibility for retention.
- Dynamic architectures adapt model structure or parameter usage to learn new concepts without interfering with old ones.
- Explicit dynamics architecture: Explicit architectural methods add, clone, or preserve model parameters; progressive networks create a connected new model for each task.
- Explicit dynamics architecture: Progressive networks require all previously created networks and the task index at test time to select the appropriate output.
- Implicit architecture modification: Implicit architectural modification adapts models without changing their architecture, including inactivating units, changing paths, or freezing weights.
- Implicit architecture modification: Weight freezing protects selected parameters from future updates but requires balancing retention against sufficient capacity for learning new skills.
- Implicit architecture modification: PathNet uses task-specific paths so newly learned tasks avoid modifying already learned weights.
- Dual architectures: Dual architectures separate a flexible model for the current task from a stable model that stores past experience.
- Dual architectures: Dual architectures can be extended with embedding models or additional components that select between flexible and stable memories.
4.2 Regularization Approaches
Regularization approaches modify weight updates to preserve previous knowledge while learning new tasks. They range from simple constraints to methods that estimate and protect important weights, but excessive regularization can eventually limit learning.
- Regularization modifies weight updates so learning a new problem preserves memory of previous knowledge.
- Simple techniques such as sparsification, dropout, and early stopping reduce weight changes and therefore the probability of forgetting.
- Fisher-based importance estimates enable Elastic Weight Consolidation to protect important weights during subsequent learning.
- Conceptor mechanisms store learned patterns and guide gradients by favoring some weight modifications while penalizing others.
- Regularization has been applied to reinforcement learning, classification, and generative models, but excessive regularization may saturate the model after several tasks.
4.3 Rehearsal Approaches
Rehearsal preserves past knowledge by storing and reusing raw samples, whereas generative replay learns past distributions and regenerates prior experiences. These strategies offer different memory and privacy trade-offs and can also support distillation or gradient regularization.
- Rehearsal: Rehearsal approaches save raw samples as memories of past tasks and reuse them during later training.
- Rehearsal: Representative samples may be organized into a coreset that dynamically adapts feature-extractor weights and strengthens connections for learned memories.
- Rehearsal: Coresets can support replay, distillation, Bayesian priors, or gradient regularization through GEM and A-GEM.
- Rehearsal: Rehearsal requires separate memory for raw unprocessed data, raising privacy concerns while preventing memories from degrading over time.
- Generative Replay: Generative replay trains generative models to sample past experience alongside current data, using GANs or auto-encoders.
- Generative Replay: Classical generative replay uses a frozen past model and a current model that learns from both regenerated and actual samples.
- Generative Replay: Marginal replay uses standard generators, while conditional replay can generate data for a specified class or task.
4.5 Hybrid Approaches
Continual learning methods commonly combine slow stable-memory mechanisms with fast learning mechanisms. Hybrid strategies reflect this combination by integrating multiple approaches, whose relationships are summarized in the paper’s strategy overview.
- Hybrid Approaches: Most continual learning approaches use implicit dual architectures with stable and fast learning mechanisms.Examples include sample memories, generative models, and Fisher matrices as stable components.
- Hybrid Approaches: Combining strategies can address the advantages and disadvantages of individual approaches to catastrophic forgetting.
- Hybrid Approaches: Figure 2 organizes popular methods across four approaches and shows rehearsal and generative replay as subsets of replay strategies.
5 Evaluation of Continual Learning Algorithms
Evaluation of continual learning requires task sequences, benchmarks, and metrics that capture more than final accuracy. The paper organizes protocols and proposes measures for forgetting, transfer, memory, computation, and stability, while noting limits for generative-model evaluation.
- Evaluation Protocols and Benchmarks: Task difficulty depends on individual task learnability, inter-task similarities, and the availability of data across the sequence.
- Evaluation Protocols and Benchmarks: Continual learning benchmarks construct task sequences by splitting, modifying, or concatenating datasets, although only a few are designed specifically for continual learning.
- Evaluation Protocols and Benchmarks: Robotics datasets recorded online through video can evaluate continual learning for object-manipulation classification and detection.
- Metrics: A rigorous evaluation should assess learning and forgetting speed, knowledge transfer, stability, efficiency, and final performance rather than final accuracy alone.
- Metrics: Accuracy, BWT, and FWT use entries of a training-test accuracy matrix to measure performance, backward influence, and forward transfer across tasks.
- Resource Metrics: Memory, sample-storage, and computational-efficiency metrics quantify model parameters, replay storage, and operations used during continual learning.
- Resource Metrics: CLscore and CLstability aggregate weighted criteria, while stability is especially relevant for robotic and safety-critical settings because abrupt performance drifts are concerning.
6 Continual Learning for Robotics
The paper frames robotics as a natural but difficult setting for continual learning because robots learn from real-world streams under hardware, data, labeling, stability, and resource constraints. It surveys robotic applications and connects them to continual-learning challenges and opportunities.
- Why robotics matters: Robots cannot revisit past states, while their power, memory, and computation constraints make them natural but demanding continual-learning platforms.Their interaction with the real world and rich multimodal sensor experience can support representation learning, but embedded resources require careful management.
- Robotics hardware: Robot hardware failures, manual recovery, cost, and limited energy impose delays and restrict long-running experiments.Hardware instability and fragile platforms can interrupt experiments, while robots may require manual recharging to continue operating.
- Data sampling: Robots must collect training data in the real world, using exploration, intrinsic motivation, curiosity, self-supervision, or online storage and processing.Data can be learned from the robot’s own interaction and either processed on the fly or stored for later use.
- Data labeling: Annotations and external help can improve learning but are expensive and time consuming, motivating few-shot and active-learning approaches.Relevant labels include object labels, reward or self-supervision signals, and task identifiers indicating task changes and objectives.
- Learning algorithm stability: Continual-learning algorithms require stability because failures can corrupt memories and degrade performance on later tasks.The paper highlights the instability of generative and reinforcement-learning models as a major concern in sequential learning.
- Applications: Robotics applications include manipulation, exploration, autonomous driving, perception, SLAM, navigation, and reinforcement-learning policy learning.Continual-learning work in robotics spans object recognition, visual saliency, semantic segmentation, object detection, mapping, navigation, and task policies.
7 Discussion and Conclusion
The paper proposes a generalized framework, evaluation metrics, and practical recommendations to characterize, compare, and transfer continual-learning approaches across machine learning and robotics. Its guidance addresses online operation, scalability, memory, reporting, reproducibility, distribution shifts, ablations, and benchmark realism.
- Framework and transfer: The framework characterizes continual-learning settings and helps transfer approaches between machine learning and robotics.It is intended to clarify problems, constraints, assumptions, and expected performance across fields.
- System constraints: The recommendations require online capabilities, bounded model-complexity growth, evaluation on more than two tasks, and realistic memory limits.These constraints target deployment in continual-learning systems whose task streams and resources may be open-ended.
- Evaluation: Evaluations should report final performance, forward and backward transfer, remembering capacity, memory, storage, computation, continual-learning score, and stability.Publicly available offline baselines are recommended to support fair and reproducible metric computation.
- Reporting and benchmarks: The paper recommends ablations, formal descriptions of distribution-shift handling, realistic high-resolution benchmarks, and precise reporting of learning assumptions.MNIST and CIFAR100 are described as limiting choices for realistic robustness assessment.
- Conclusion: Overall, the paper reviews continual-learning strategies, benchmarks, and evaluation techniques to improve categorization, comparison, and connections with robotics.The authors present formalization as important for enabling the two fields to learn from each other’s successes.