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The Limits and Potentials of Deep Learning for Robotics

Niko Sünderhauf, Oliver Brock, Walter Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, Peter Corke

arXiv:1804.06557v1cs.RO

TL;DR

Deep learning for robotics faces problems that standard computer-vision benchmarks and methods do not fully address. This paper surveys robotics-specific learning, embodiment, reasoning, evaluation, simulation, and model-integration challenges, highlighting both limitations and research directions.

  • Problem

    Robotics requires learning and reasoning for active interaction with the uncertain physical world, where benchmark performance may not predict real-world behavior and failures can be consequential.

  • Method

    The paper synthesizes workshop perspectives to survey robotics-specific challenges, evaluation approaches, simulation issues, and the relationship between deep learning and physics-based models.

  • Results

    The paper identifies open research directions spanning better evaluation, simulation-to-real learning, integrated embodiment and reasoning, and combinations of deep learning with model knowledge.

  • Takeaways & Limitations

    Progress toward integrated robotic learning should exploit embodiment, prior knowledge, semantic and geometric structure, and operation under open-set conditions.

Abstract

from arXiv · show

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and help fulfill the promising potentials of deep learning in robotics.

I. INTRODUCTION

Deep learning in robotics raises challenges beyond typical computer-vision questions because robots act in uncertain real-world conditions where mistakes can be catastrophic. The paper surveys these challenges and open questions, including data requirements, transfer, physical structure, and model- versus data-driven reasoning.

  • Robots must perceive, decide, plan, and act using incomplete and uncertain knowledge in uncontrolled conditions, where mistakes can endanger missions and human lives.
  • Robotic deep learning raises questions about trusting predictions, estimating uncertainty, fusing outputs with prior knowledge and sensors, and handling realistic open-set scenarios.
  • Key learning questions concern obtaining enough high-quality training data and ensuring policies transfer between simulation and reality or across robots.
  • The paper asks how deep learning can exploit task structure, constraints, and physical laws, and whether model-driven and data-driven methods form a spectrum.
  • The paper offers workshop participants’ perspectives on robotics-specific limits, potentials, and open research problems to outline directions for greater future impact.

II. CHALLENGES FOR DEEP LEARNING IN ROBOTIC VISION

Robotic vision differs from computer vision because perception is embedded in an active system that converts images into real-world actions. Its challenges span learning, embodiment, and reasoning, including uncertainty and open-set operation.

  • Robotic vision is part of an embodied, active, goal-driven system whose perceptual outputs ultimately produce actions in the physical world.
  • The paper organizes robotic-vision challenges along three conceptually orthogonal axes: learning, embodiment, and reasoning.
  • Reliable uncertainty estimates would let robots treat deep networks as sensors and fuse predictions with prior knowledge or other measurements using Bayesian techniques.
  • Calibration and Bayesian deep-learning methods, including dropout sampling and ensembles, are current approaches to uncertainty estimation.
  • Robots frequently encounter classes and conditions absent from training data, so open-set systems must identify unknowns instead of assigning them high-confidence known-class labels.

3) Incremental Learning:

Incremental learning addresses changing deployment conditions by adapting representations to new examples and classes while preserving prior knowledge. Active learning further seeks to select informative samples efficiently for human labeling.

  • 3) Incremental Learning:: Domain adaptation requires robotic-vision systems to learn from new samples of known classes and update internal representations for deployment conditions.
  • 3) Incremental Learning:: Class-incremental learning extends knowledge to new classes without forgetting previous representations, preferably through one-shot or low-shot learning.
  • 3) Incremental Learning:: Current class-incremental methods still require users to identify which samples are new data for incorporation.
  • 3) Incremental Learning:: Active learning aims to select the most informative samples autonomously, reducing costly human labeling through data-efficient interaction.
  • 3) Incremental Learning:: Some approaches use approximate Bayesian uncertainty estimates to choose informative samples for learning.

B. Embodiment Challenges

Embodiment gives robotic vision temporal and spatial information unavailable to systems treating images independently. Exploiting motion and viewpoint changes can improve perception, but changing scenes and occlusions create additional challenges.

  • 1) Temporal Embodiment:: Temporal embodiment lets robotic vision accumulate evidence over correlated frames and exploit viewpoint variation, yet its potential is rarely used.
  • 1) Temporal Embodiment:: CORe50 is among the few datasets designed to encourage temporal embodiment for object recognition.
  • 1) Temporal Embodiment:: Temporal embodiment must handle scene changes caused by moving objects, lighting, seasonal structure, and changing object presence or pose.
  • 2) Spatial Embodiment:: Spatial embodiment exposes cameras to changing viewpoints that can disambiguate semantics, improve depth, and separate objects in clutter.
  • 2) Spatial Embodiment:: Occlusions and sudden appearance changes from movement require capabilities such as object unity and object permanence.

3) Active Vision:

Active vision uses a robot’s ability to control its viewpoint—and potentially the scene itself—to gather information and improve perception. The paper argues that holistic active scene understanding remains an open research problem.

  • Robotic vision can control camera movement and viewpoint to improve perception or gather additional scene information.
  • Existing work addresses next-best viewpoints for object detection and exploration, but a holistic active scene-understanding approach is still missing.
  • Robots could manipulate occluding objects to reveal hidden surfaces, requiring geometric understanding and reasoning about perception-improving scene changes.
  • Active robotic vision is framed as a reasoning challenge involving separate and joint reasoning about scene semantics and geometry.
  • Semantic regularities and contextual information could provide prior knowledge that helps disambiguate and improve robustness in robotic perception.

2) Reasoning About Object and Scene Geometry:

Robotic vision must infer object and scene geometry in cluttered, open-set environments while representing uncertainty and using embodiment to collect better information. The paper emphasizes tightly coupling semantic and geometric reasoning because each can inform the other.

  • Robotic applications commonly involve cluttered scenes, whereas many single-image geometry methods are evaluated on isolated prominent objects.
  • Geometry-based grasp planning should express uncertainty in inferred object shape and exploit camera movement to collect information for more accurate estimates.
  • Scene-geometry inference supports reasoning about object poses, support surfaces, and object continuity despite occlusions.
  • Joint semantic-geometric inference is important in realistic open-set environments because semantics and geometry can co-inform each other.
  • Robotic evaluation must account for dynamic environments and generalization across novel settings rather than relying only on benchmark summary statistics.

The Importance of Open Set Recognition

Open-set recognition requires models to handle unknown classes and conditions that are absent from training. The paper links this challenge to decision-boundary design and advocates evaluation methods that reveal failures across varied appearances and conditions.

  • Open-set recognition includes known classes, labeled negative examples, and unknown classes unseen during training.
  • Linear SVMs can assign positive labels to distant unknown samples, while Softmax and cosine-similarity thresholds rely on known-class information.
  • New algorithms should reduce risk from unknowns through bounded decision regions, outlier modeling, or extreme-value calibration, alongside evaluations containing known and unknown data.
  • Rotation, scale, and occlusion can expose recognition failures that exhaustive appearance testing typically does not measure.
  • Visual psychophysics can generate item-response curves from systematically perturbed images or rendered scenes, enabling comparisons across models and with humans.
  • Human performance exceeded model performance in face detection despite both appearing to reach the dataset’s summary-statistic ceiling.

IV. THE ROLE OF SIMULATION FOR PIXEL-TO-ACTION ROBOTICS

Simulation can make pixel-to-action learning feasible, but transferring policies to real robots remains difficult because of the reality gap and costly real-world interaction. Progressive networks offer one approach to transfer simulation-learned features and policies while accommodating differences between simulated and real sensors.

  • Simulation and sim-to-real transfer: 50 million interaction steps are required for full performance on a simulated Jaco reaching task, making equivalent real-robot training infeasible.The task uses A3C and randomly placed targets and start locations.
  • Simulation and sim-to-real transfer: Raw pixel inputs expose a reality gap between simulated tasks and their real-world analogues, motivating transfer-learning methods for robotics.The gap is illustrated by differences between real camera images and MuJoCo-rendered images.
  • Simulation and sim-to-real transfer: Progressive networks transfer learned policies and features through lateral connections while freezing earlier columns, preserving prior knowledge across tasks.A later column can be trained on the real robot or an additional task using frozen policies and features from earlier columns.
  • Simulation and sim-to-real transfer: 34 points are reached by the progressive second column on the Jaco robot, whereas fine-tuning does not reach the same score.The comparison supports the value of the progressive architecture for sim-to-real transfer.
  • Model-driven and data-driven approaches: Deep learning can learn predictive physics models and control policies from experience, but current approaches remain far from human prediction and control capabilities.Key challenges include generalizing predictive models, using physics-based models to reduce data needs, and reusing models and controllers across abstraction levels.
  • Model-driven and data-driven approaches: Model-based and deep-learned approaches differ across representations, generality, robustness, data efficiency, and computational efficiency.Model-based techniques explicitly represent physical properties, while deep-learning models typically encode them implicitly in networks and parameters.

VI. TOWARDS AN AUTOMATION OF INFORMATICS

Deep learning success challenges algorithm design as the dominant problem-solving paradigm. The paper frames programming and data-driven learning as opposite ends of a spectrum, with intermediate solutions combining their contributions.

  • VI. THE ROLE OF DEEP LEARNING IN INFORMATICS: Deep learning has outperformed prior explicitly programmed image-processing approaches in image classification, challenging algorithm design as the dominant paradigm.The paper presents this as part of a broader change in computer science foundations.
  • VI. THE ROLE OF DEEP LEARNING IN INFORMATICS: The term algorithm includes processes or rules for calculations and problem-solving, including physics formulae, computational models, probabilistic representations, and inference.This definition broadens algorithm design beyond conventional programming procedures.
  • VI. THE ROLE OF DEEP LEARNING IN INFORMATICS: Programming and data-driven learning form a spectrum, from expert-written problem-specific algorithms requiring no additional data to generic methods that learn solutions from large datasets.The paper places traditional computer science and deep learning at opposite ends of this spectrum.

Programming versus data

The paper presents programming and deep learning as complementary ends of a spectrum between explicit problem understanding and data-driven solution discovery. It argues for combining both to uncover, represent, and reuse problem structure.

  • Programming versus data: Programming and deep learning occupy opposite ends of a spectrum in which expert-designed algorithms and large-scale data contribute differently to computational solutions.Intermediate approaches can combine programming and data-driven learning.
  • Programming versus data: At the deep-learning end, programming is replaced by training, while network architecture, loss functions, procedures, and data express the solution.The training procedure itself remains a concrete algorithm.
  • Programming versus data: Solutions positioned toward deep learning require less explicit understanding of the problem, whereas programming reflects human understanding through articulated structure.The paper argues that scientific understanding should favor explicit structure where possible.
  • Programming versus data: A learned solution that outperforms an established Bayes filter would raise questions about whether the filter adequately captures the problem’s structure.The quad-copter trajectory example illustrates how learning can expose previously unrecognized structure.
  • Programming versus data: The paper recommends moving toward the programming end where possible, using generic learning tools to discover structure, and dividing problems into known and discoverable components.This component-wise strategy supports movement toward explicit understanding.
  • Programming versus data: Generic learning methods can implicitly learn problem knowledge, motivating methods that extract and algorithmify structure from neural networks.The paper calls for detailed study of network limitations and parameter dependencies; distillation is identified as promising but not yet algorithmifiable.
  • Programming versus data: Generalization insights may not transfer easily to neural networks, and networks might memorize training data rather than extract problem structure.If memorization dominates, new regularization methods may be needed, although evidence also suggests networks can learn useful representations.

Complex problems should be solved by decomposition and re-composition

Complex robotic problems can be decomposed into sub-problems solved with methods from different points on the programming/data spectrum, then recomposed. The paper also questions whether all problems are decomposable and suggests task-, agent-, and environment-specific factorizations.

  • Complex problems can contain sub-problems whose solutions appropriately occupy different points along the programming/data spectrum.The paper argues that understanding a sub-problem’s inherent structure should guide whether it is handled algorithmically or through learning.
  • Algorithmic solutions suit well-understood tasks, while deep neural networks are appropriate for problems such as image classification where they outperform human programs.
  • Solutions from different spectrum positions can be recomposed using differentiable versions of existing algorithms compatible with back-propagation.One example combines a histogram filter with learned motion and sensing models for quad-copter localization.
  • End-to-end learning can improve a solution, but it remains unclear whether this reflects overfitting, poor initial factorization, or genuinely complex interactions between sub-problems.
  • The paper suspects an optimal factorization exists for each defined task, agent, and environment, although interfaces between sub-problems may not be simple.
  • Automated programming would require decomposition, heterogeneous sub-problem solvers, recomposition, and extraction of algorithmic information from data-driven solutions.
  • Programs and learned weights may differ mainly in expressiveness and prior assumptions, while some problems may lack algorithmifiable structure and remain data-driven.

Priors to reduce the amount of data

Robotic learning requires priors and integrated approaches because real-world interaction data is costly. The paper frames deep learning’s broader impact as recognition of a spectrum between programming and data-driven learning.

  • Acquiring large amounts of data through real-world interaction is costly, so appropriate priors are needed to reduce the data required for learning.Priors restrict possible interpretations to those consistent with prior knowledge.
  • Data can be acquired in less restrictive settings when learning agents automatically separate samples associated with different tasks.
  • Deep learning’s broader impact may be the recognition that programming and data form a spectrum rather than a dichotomy.
  • The paper’s workshop-based perspective identifies open research problems and directions for increasing deep learning’s impact in robotics.
  • Future systems should integrate perception, action, embodiment, prior model knowledge, heuristics, and semantic understanding for complex multi-stage tasks.
  • The paper surveys research questions and challenges in robotic perception, learning, and control, pointing toward directions for further research.
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