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Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation

Annie Xie, Lisa Lee, Ted Xiao, Chelsea Finn

arXiv:2307.03659v1cs.ROcs.AI

TL;DR

Imitation-learning policies can struggle to generalize across new visual environments, while prior work offers limited evidence about how much individual factors contribute to the gap. This paper evaluates factor shifts on a real robot and in a 19-task, 11-factor simulation benchmark, finding a consistent ordering of factor difficulty across the two settings.

  • Problem

    Prior work provides limited evidence about how much individual environmental factors contribute to the generalization gap in visual robotic manipulation.

    Source: §1 · p. 1

  • Method

    The study evaluates imitation-learning policies across environmental shifts on a real robot and in Factor World, a simulated benchmark of 19 tasks and 11 customizable factors.

    Source: §1 · p. 2

  • Results

    The factor difficulty ordering is consistent across simulation and the real-robot setup; most factor pairs do not compound, and crop augmentation and out-of-domain visual diversity improve generalization.

    Source: §1 · p. 2, §6 · p. 8

  • Takeaways & Limitations

    Because most factor pairs do not compound, environment factors can be studied and addressed individually.

    Source: §1 · p. 2

    The study covers only a few representative robotic tasks and environmental factors, and expanding the real-robot scope is challenging because of the required number of evaluations.

    Source: §6 · p. 8

Abstract

from arXiv · show

What makes generalization hard for imitation learning in visual robotic manipulation? This question is difficult to approach at face value, but the environment from the perspective of a robot can often be decomposed into enumerable factors of variation, such as the lighting conditions or the placement of the camera. Empirically, generalization to some of these factors have presented a greater obstacle than others, but existing work sheds little light on precisely how much each factor contributes to the generalization gap. Towards an answer to this question, we study imitation learning policies in simulation and on a real robot language-conditioned manipulation task to quantify the difficulty of generalization to different (sets of) factors. We also design a new simulated benchmark of 19 tasks with 11 factors of variation to facilitate more controlled evaluations of generalization. From our study, we determine an ordering of factors based on generalization difficulty, that is consistent across simulation and our real robot setup.

1 Introduction

The paper asks which environmental changes most challenge generalization in visual robotic manipulation, and studies these changes as separable factors. It evaluates real-robot and simulated settings, introduces Factor World, and reports findings about factor combinations, augmentation, and visually diverse data.

1 Introduction

  • Prior augmentation and pretrained representations improve performance, but a generalization gap remains and there is no consensus on what data is needed.The paper frames its question around identifying which environmental dimensions existing models struggle with.Source: §1 · p. 1
  • New camera positions are hardest to generalize to, while new backgrounds are easiest, according to the real-robot success-rate comparison.The figure summarizes success rates across environment shifts.Source: Figure 1 · p. 1
  • The study evaluates over 20 real-robot test scenarios and introduces Factor World, a simulated suite of 19 tasks with 11 customizable factors and over 100 configurations per factor.The benchmark is intended to support finer-grained evaluations of generalization.Source: §1 · p. 2
  • Most pairs of environment factors do not compound the generalization difficulty, suggesting that factors can be studied and addressed individually.For example, new table textures combined with new distractors are no harder than new table textures alone.Source: §1 · p. 2
  • Random crop augmentation improves generalization to spatial shifts such as camera position and non-spatial shifts such as distractors and table textures.The authors characterize it as a lightweight way to improve generalization.Source: §1 · p. 2
  • Training on visually diverse out-of-domain data, including demonstrations from other tasks, improves performance on tabletop object picking.Examples include opening a fridge and operating a cereal dispenser.Source: §1 · p. 2

2 Related Work

Prior work studies robotic generalization through diverse datasets, pretrained representations, and data augmentation, but collecting diverse data at scale remains costly. This paper positions Factor World as a more extensive factor-variation benchmark than KitchenShift.

Datasets and benchmarks

  • Factor World covers 11 factors with over 100 configurations each, compared with KitchenShift’s 7 factors and fewer than 10 configurations each.The paper presents this breadth as a more complete factor set and many more configurations.Source: §2 · p. 2
  • Robotics research uses datasets spanning objects, domains, and tasks, but collecting high-quality diverse data at scale remains an unsolved challenge.The paper aims to inform how new data should be collected given its cost.Source: §2 · p. 2

Pretrained representations and data augmentation

  • Pretrained representations from large non-robotics datasets can support data-efficient robotic policies, while image augmentations such as random shifts, color jitter, and rotations have improved generalization.The paper seeks to examine when and why these approaches help through factor decomposition.Source: §2 · p. 2

Generalization in RL

  • Unlike low-dimensional studies of physical parameters such as agent mass, this work evaluates observable environment changes in image-based robotic tasks.The passage notes that changes such as table position are visible to the agent.Source: §2 · p. 3

3 Environment Factors

The paper formalizes environmental shifts as controllable factors because prior observations of policy robustness were often qualitative or underspecified. It evaluates selected factors on a real robot and implements a broader set in Factor World.

3 Environment Factors

  • Prior studies report robustness patterns for lighting, backgrounds, and distractors, but findings are often qualitative or cannot isolate appearance from physical differences.The paper aims to formalize these observations with more comprehensive, fine-grained environmental shifts.Source: §3 · p. 3

3.1 Real Robot Manipulation

  • Real-robot evaluations vary lighting, distractor objects, backgrounds, table textures, and camera pose; lighting uses filtered LED sources, while patterned paper changes surfaces and backgrounds.Camera pose is shifted by changing the robot head orientation; table and camera position are reserved for simulation because of practical challenges.Source: §3.1 · pp. 3–4

3.2 Factor World

  • Factor World is a suite of 19 visually diverse manipulation tasks configurable across factors such as lighting, object properties, surfaces, camera and table position, and distractors.The figure depicts the range of task and environment variation in the benchmark.Source: Figure 4 · p. 4
  • Factor World builds on Meta World with 11 environment factors, including lighting, object properties, surfaces, camera and table position, arm pose, and distractors.The study excludes object size and shape because designing an expert policy for any object is harder, and excludes initial arm pose because it can usually be fixed.Source: §3.2 · p. 4

4 Study Design

The study evaluates how environmental factors affect generalization in imitation learning, using real-robot and simulated manipulation tasks. It specifies the datasets, policies, and evaluation protocols used to measure that behavior.

4 Study Design

  • The design aims to measure how individual environment factors affect generalization under scenarios resembling those practitioners encounter.The study selects commonly studied robotics tasks and describes its data collection, algorithms, and evaluation protocol.Source: §4 · p. 4

4.1 Control Tasks and Datasets

  • The real-robot evaluation focuses on language-conditioned picking, using over 115K human-collected demonstrations across 13 skills, 100+ objects, three tables, and three locations.The robot receives six recent 300 × 300 RGB images and an instruction; actions span arm and base movements across 10 discretized dimensions.Source: §4.1 · p. 4
  • The Factor World study focuses on pick-place, bin-picking, and door-open tasks from a 19-task simulated manipulation suite.Scripted expert policies generate demonstrations; observations include images and recent end-effector state, and actions specify end-effector movement and gripper opening or closing.Source: §4.1 · p. 5

4.2 Algorithms and Evaluation Protocol

  • The real-robot policy uses RT-1, while simulated behavior cloning is tested with random-crop and photometric augmentation and CLIP or R3M image representations.RT-1 tokenizes images, text, and actions and uses a Transformer with a language-conditioned imitation-learning objective.Source: §4.2 · p. 5
  • The real-robot protocol averages success over 12 trials per factor; Factor World reports the average generalization gap across 100 test environments per factor.The gap is defined as train-environment success minus success under shifts to factor F.Source: §4.2 · p. 5

5 Experimental Results

The experiments compare environmental shifts, augmentation and pretraining, and data-collection strategies. They find that generalization difficulty varies by factor, while visual diversity from out-of-domain data can improve performance across shifts.

5.1 Impact of Environment Factors on Generalization

  • Table textures and camera positions were harder to generalize to than distractors and lighting in both settings, while backgrounds were easiest on the robot but harder in Factor World.In Factor World, new object textures were about as hard as camera positions, and table positions were as hard as table textures.Source: §5.1 · pp. 5–6, Figure 5 · p. 6
  • In Factor World, 16 of 21 factor pairs had relative success-rate differences between −6% and 6%, indicating little compounding for most pairs.Object texture with distractors and lighting with distractors were synergistic exceptions, making generalization easier than to the harder individual factor.Source: §5.1 · pp. 6–7, Figure 5 · p. 6
  • For camera and table positions, the generalization gap roughly doubled as the sampled range radius increased from 0.025 to 0.075 meters.The authors suggest wider ranges may require more training-set diversity.Source: Figure 5 · p. 6, §5.1 · p. 7

5.2 Effect of Data Augmentation and Pretrained Representations

  • Random crops improved generalization to spatial and non-spatial shifts, while photometric distortions improved performance on texture-based factors.Crop improvements were most significant for new camera positions and table textures; photometric improvements included table texture on the robot and object, table, and background in simulation.Source: Figure 6 · p. 6, §5.2 · p. 7, Figure 7 · p. 7
  • R3M and CLIP policies performed well in training environments but retained large generalization gaps across many shifts; CLIP improved over a scratch-trained CNN on new object textures.Figure 7 reports Factor World generalization gaps averaged across seven factors, three tasks, and five seeds.Source: §5.2 · p. 7, Figure 7 · p. 7

5.3 Investigating Different Strategies for Data Collection

  • Across the different environment shifts, the policy trained with the full out-of-domain robotics dataset was more successful across the board, while the in-domain-only policy’s success rate dropped significantly.Only 35.2% of the 115K demonstrations were collected in the same domain as the evaluations.Source: §5.3 · pp. 7–8
  • At equal dataset size, the out-of-domain small dataset performed comparably to in-domain-only data on most factors, but new-distractor success was 44.4% versus 75.0%.The small mixed dataset contained significantly fewer in-domain demonstrations of the pick skill.Source: §5.3 · p. 8, Figure 8 · p. 8

6 Discussion

The study finds a consistent ordering of environmental factors by generalization difficulty across simulation and the real-robot setup. Its scope is limited to a few representative tasks and factors, and expanding real-robot evaluations is challenging because they require many experiments.

6 Discussion

  • The ordering of environmental factors by generalization difficulty is consistent across simulation and the real-robot setup.The study also quantifies the impact of solutions such as data augmentation.Source: §6 · p. 8
  • The study covers a few representative robotic tasks and factors; its 1,440 real-robot evaluations make broadening the scope challenging.The authors suggest using Factor World to study additional tasks, factors, and reinforcement learning, and identify higher-capacity models as a next step after slight training-environment performance degradation with more varied training environments.Source: §6 · p. 8

A.1 Experimental Details

This section provides additional details on the experimental setup and evaluation metrics.

A.1 Experimental Details

  • The section provides additional details on the experimental setup and evaluation metrics.Source: §A.1 · p. 13

A.1.1 Experimental Setup

The experimental setup includes real-world picking tasks and simulated environmental factors. Factor World enumerates its variation factors and specifies ranges for continuous-valued factors.

A.1.1 Experimental Setup

  • The real-robot evaluation defines six real-world picking tasks, visualized in Figure 9.The passage names Pepsi can, water bottle, blue chip bag, green jalapeno chip bag, and Oreo.Source: §A.1.1 · p. 13, Figure 9 · p. 13
  • Factor World’s implemented variation factors are enumerated in Figure 10, and Table 1 specifies ranges for continuous-valued factors.Source: §A.1.1 · p. 13
  • Table 1 gives continuous-factor ranges in meters and compares position-based factors with a 0.7m × 0.4m environment table.Source: Table 1 · p. 13

A.1.2 Dataset Details

The simulated datasets vary the number of training environments for pick-place and use smaller demonstration sets for bin-picking and door-open, which the authors found easier.

A.1.2 Dataset Details

  • Pick-place datasets contain 2,000 demonstrations across N = 5, 20, 50, or 100 training environments.Each training environment is parameterized by one value for each environment factor.Source: §A.1.2 · p. 13
  • Bin-picking and door-open datasets each contain 1,000 demonstrations; the authors found these tasks easier than pick-place.Source: §A.1.2 · p. 13

A.1.3 Evaluation Metrics

The appendix defines measures of generalization and details the RT-1 and Factor World implementations. Additional simulations compare representations and report factor-pair and success-rate results.

A.1.3 Evaluation Metrics

  • The generalization gap is train-environment performance minus test-environment performance, with one or two factors assigned new values in test environments.Source: §A.1.3 · p. 13
  • For factor pairs, percentage difference compares combined-shift success with the lower success rate under either individual shift.Source: §A.1.3 · p. 14

A.2.1 RT-1

  • RT-1 behavior cloning fuses language and six recent RGB observations through pretrained encoders before tokenized action decoding.Training-only augmentation applies photometric jitter and independently sampled 300×300 crops across the six-frame history.Source: §A.2.1 · pp. 14–15

A.2.2 Factor World

  • Factor World policies use a four-layer CNN and three-layer 256-unit head, trained 100 epochs; experiments add shift or color-jitter augmentation or ResNet50 R3M/CLIP representations.Source: §A.2.2 · p. 15

A.3.1 Simulation: Data Augmentation and Pretrained Representations

  • R3M fails to generalize to most factors except new distractors, while CLIP performs similarly to a CNN except that it outperforms the CNN on new object textures.Source: §A.3.1 · p. 15, Figure 11 · p. 16

A.3.2 Simulation: Factor Pairs

A.3.3 Simulation: Success Rates

  • As training environments increase, factor-shifted success rates rise, while some policies lose success on original environments; larger-capacity models may improve further.Source: §A.3.3 · pp. 15–16, Figure 13 · p. 17

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