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Language-Driven Representation Learning for Robotics

Siddharth Karamcheti, Suraj Nair, Annie S. Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, Percy Liang

arXiv:2302.12766v1cs.ROcs.AIcs.CLcs.CVcs.LG

TL;DR

Visual representations learned from human videos have shown promise for robotic control but remain inconsistent across the broader range of robot-learning problems. Voltron combines language-conditioned reconstruction and grounded language generation, and its representations outperform prior approaches across a five-domain evaluation suite, especially on tasks requiring higher-level features. The paper also identifies scale and comparability constraints and acknowledges that no single representation is strong on every task.

  • Problem

    Existing visual representations show inconsistent performance across robotics problems beyond control, motivating evaluation of both spatial and semantic capabilities.

  • Method

    Voltron learns from captioned human videos by balancing language conditioning and generation on a masked autoencoding backbone, evaluated across five robotics domains.

  • Results

    Voltron models outperform prior approaches across all evaluation tasks, with stronger gains on applications requiring higher-level features.

  • Takeaways & Limitations

    Language-driven representations provide a unified way to balance low-level spatial reasoning and high-level semantic understanding for diverse robotics applications.

  • Takeaways & Limitations

    The study does not train models at the scale and data volume of prior work, and no single representation is strong on every task.

Abstract

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Recent work in visual representation learning for robotics demonstrates the viability of learning from large video datasets of humans performing everyday tasks. Leveraging methods such as masked autoencoding and contrastive learning, these representations exhibit strong transfer to policy learning for visuomotor control. But, robot learning encompasses a diverse set of problems beyond control including grasp affordance prediction, language-conditioned imitation learning, and intent scoring for human-robot collaboration, amongst others. First, we demonstrate that existing representations yield inconsistent results across these tasks: masked autoencoding approaches pick up on low-level spatial features at the cost of high-level semantics, while contrastive learning approaches capture the opposite. We then introduce Voltron, a framework for language-driven representation learning from human videos and associated captions. Voltron trades off language-conditioned visual reconstruction to learn low-level visual patterns, and visually-grounded language generation to encode high-level semantics. We also construct a new evaluation suite spanning five distinct robot learning problems $\unicode{x2013}$ a unified platform for holistically evaluating visual representations for robotics. Through comprehensive, controlled experiments across all five problems, we find that Voltron's language-driven representations outperform the prior state-of-the-art, especially on targeted problems requiring higher-level features.

1 Introduction

Voltron broadens visual representation evaluation beyond visuomotor control to five robotics problems requiring both spatial and semantic understanding. Its language-driven representations outperform prior approaches across domains, while model design exposes tradeoffs between low- and high-level features.

  • Existing representation objectives encode different priorities: masked autoencoding favors low-level spatial reconstruction, whereas contrastive learning favors higher-level semantics.
  • Voltron learns from videos and captions by combining masked visual reconstruction with language conditioning or generation.
  • Robot learning spans grasp affordance prediction, referring expression grounding, visuomotor control, language-conditioned imitation, and intent scoring.
  • V – Cond strictly outperforms MVP and R3M across all evaluation domains under controlled pretraining-data and model-capacity comparisons.
  • Generation-oriented and multi-frame variants further improve higher-level evaluations, but language generation can hurt some control tasks and no single representation dominates every task.
  • The paper contributes Voltron, a five-domain evaluation suite, tradeoff analyses, and released models, code, data, and evaluation resources.
  • The comparisons reproduce MVP and R3M under controlled conditions, while official artifacts and other methods are marked as not directly comparable.

2 Related Work

Prior robotics representations draw on task-specific data, visual pretraining, and multimodal learning, but their objectives encode different capabilities. Voltron instead balances language conditioning and generation to produce representations that transfer across robotics applications.

  • Unlike task-specific approaches, this work leverages large human-video datasets to learn visual representations for robotics.
  • Generalization requires representations to relate observations and their changes to higher-level semantic abstractions, not merely summarize visual inputs.
  • Voltron jointly models frame sequences and language, supporting single-image representations and language generation grounded in visual context.
  • Multimodal pretraining can enrich visual representations through language supervision, including when language is absent downstream.
  • Voltron differs from related multimodal approaches by balancing language conditioning and generation for transfer across robotics applications.

3 Voltron – Language-Driven Learning

Voltron learns visual representations from paired videos and language by balancing masked visual reconstruction with language generation. Its variants vary language usage and temporal context to trade off low-level spatial and higher-level semantic features.

  • Voltron assumes videos paired with captions, narrations, or coarse textual behavior labels as training inputs.
  • The model combines a multimodal encoder, masked visual reconstructor, and language generator to shape its learned representation.
  • Language-conditioned reconstruction predicts masked visual regions, while language generation predicts annotations from visual context without giving the generator the target annotation.The <NULL> token prevents trivial encoder collapse from memorizing output language tokens.
  • The objective trades off low-level spatial information from reconstruction against higher-level semantic information from visually grounded language generation.
  • V – Cond uses single-frame conditioning, V – Dual uses dual-frame conditioning, and V – Gen combines dual frames with equal-probability conditioning and generation.The study limits frame contexts to at most two because four-frame contexts exceed available memory.
  • The authors do not evaluate α = 1 because preliminary experiments found that some language conditioning is always helpful.

4 Implementation & Reproducibility

The study reimplements prior representations under controlled data and capacity conditions, while documenting model architecture, training, and evaluation-suite details. Architectural changes improve training stability without changing downstream evaluation results.

  • The authors reimplement MVP and R3M while controlling pretraining data and model capacity, addressing missing reproduction details in prior releases.
  • MVP uses masked autoencoding on standalone frames, whereas R3M combines temporal and image-language contrastive objectives in a single-frame encoder.
  • All models use the same Something-Something-v2 image frames, with five frames extracted per video each training epoch.
  • Official R3M and MVP comparisons use substantially larger Ego4D-based datasets and larger models than the controlled training setup.The released R3M uses more than 20x the data, while released models scale to 86M and 307M parameters.
  • Transformer changes involving normalization, activation, and residual scaling improve training stability but do not change downstream evaluation results.

5 Evaluation Suite: Construction & Results

The evaluation suite spans diverse robotic perception, language, control, and intent-scoring problems. Voltron performs strongly across these domains, with especially clear gains on language-conditioned tasks, although single-task control remains difficult.

  • Evaluation Suite: The suite evaluates grasp affordance segmentation, referring-expression grounding, simulated visuomotor imitation, real-world language-conditioned policies, and intent scoring.
  • Grasp Affordance Prediction: Grasp affordance prediction segments graspable and non-graspable regions for suction-based grippers and probes low-level spatial features.
  • Grasp Affordance Prediction: Voltron outperforms contrastive representations on grasp affordance prediction, and V – Cond surpasses R-MVP and MVP (EgoSoup) despite no language input.
  • Referring Expression Grounding: Referring-expression grounding predicts object boxes across three increasing clutter levels and tests object-centric semantics such as color and spatial relationships.
  • Referring Expression Grounding: Voltron models outperform R-MVP by 40% and R-R3M by over 25% across all referring-expression clutter splits.
  • Single-Task Visuomotor Control: In Franka Kitchen control, V – Dual and V – Gen improve over single-frame approaches, but absolute success rates remain low under limited demonstrations.
  • Language-Conditioned Policies and Intent Scoring: For language-conditioned multi-task policies, Voltron receives an additional performance boost, while V – Gen coherently scores intent progress over time despite never being trained for intent scoring.

6 Ablations, Extensions, & Further Analysis

The authors use ablations and extensions to test whether language supervision drives Voltron’s gains, examine scaling, and assess robustness to visual distractors. Removing language reduces performance, while larger models improve results and Voltron remains robust under extreme distractors.

  • Ablations: Removing language causes a definitive performance drop across all evaluated applications.The no-language V–Cond variant performs comparably to R-MVP, indicating Voltron’s gains do not stem from its architecture modifications.
  • Ablations: Table 4 evaluates ablations on grasp affordance prediction, referring expression grounding, and single-task visuomotor control.
  • Scaling Up: 86M-parameter ViT-Base V–Cond models universally improve over the 22M-parameter ViT-Small variant.Top-5% precision for grasping increases by 15%, while expression grounding and control also improve.
  • Robustness to Real-World Distractors: Voltron and R-MVP remain robust to even the most extreme visual distractors in a limited evaluation.The authors associate this robustness with per-patch masking coupled with MAP-based extraction.

7 Discussion & Conclusion

The paper presents Voltron as a language-driven representation framework and a five-problem evaluation suite for robotics. Its controlled experiments show that balanced language conditioning and generation outperform prior approaches across all evaluation tasks, while important questions remain open.

  • Discussion & Conclusion: Voltron balances language conditioning and generation to shape captured low-level and high-level features.
  • Discussion & Conclusion: The evaluation suite spans five diverse robotics problems for holistic visual-representation evaluation.
  • Open Questions: The authors identify unresolved questions about why language helps non-language tasks and how to jointly encode low-level and high-level features.They position Voltron as a starting point for future improvements in visual representation learning for robotics.
  • Robustness: The distractor evaluation includes changes ranging from background-object swaps to playing a Voltron cartoon clip on a workspace tablet.

Overview

The appendices provide supplementary implementation, visualization, and discussion materials. The project also releases open-source models, preprocessing resources, and a standalone evaluation suite for downstream representation studies.

  • Appendix Overview: The appendices add implementation, pretraining, adaptation, discussion, results, and visualization details beyond the main text.
  • Released Resources: Released resources include pretrained-model loading code, preprocessing splits, training batches, and a separate evaluation-suite repository.
  • Released Resources: The modeling and evaluation repositories are open source, implemented in PyTorch, and the evaluation code can be overridden for custom use.
  • Appendix Overview: The appendix overview directs readers to the project page for videos and visualizations.
  • Appendix Overview: The appendices address motivating questions such as why the evaluation uses frozen representations and link answers to further experiments.

Appendix B – Voltron Implementation

Appendix B documents Voltron’s Transformer implementation, multimodal vision-language processing, pretraining curves, and released artifacts. It also lists pretrained model variants and a larger ViT-Base checkpoint.

  • Voltron Transformer Implementation: Appendix B.1 compares Voltron Transformer blocks side by side with standard Vision Transformer blocks.
  • Jointly Processing Vision & Language: Appendix B.2 describes jointly processing vision and language, including position encodings and modality tokens.
  • Pretraining Curves: Appendix B.3 provides Voltron pretraining curves for reconstruction and language-modeling errors over training.These curves characterize downstream-model behavior and trade-offs between the losses.
  • Index of Released Artifacts: Appendix B.4 indexes released artifacts, including pretrained V–Cond, V–Dual, and V–Gen models.The release also includes intermediate checkpoints and a larger ViT-Base V–Cond model.

Appendix C – Additional Results & Visualization

Appendix C adds analyses, ablations, qualitative visualizations, and implementation details that further examine Voltron’s training behavior and downstream performance. These results reinforce the roles of language supervision, generative objectives, and representation type across evaluation settings.

  • Language-conditioning lowers reconstruction loss and helps models learn more low-level visual features.
  • Masked language modeling shows early overfitting that affects the learned representations.
  • C.3 Results: Adroit Visuomotor Control: Adroit control results favor language-driven and higher-level representations, showing that representation preferences vary across evaluation domains.
  • Additional materials cover intent-scoring visualizations, real-robot policy rollouts, preprocessing, feature extraction, and adaptation pipelines.The appendix includes qualitative results from WHiRL and real-world language-conditioned policies, plus implementation discussions for the evaluation domains.

A Motivating Questions

The motivating questions examine why language supervision helps, how context and objective choices affect learning, and why different tasks may prefer different visual features. The responses also discuss frozen evaluation, data requirements, and implementation choices affecting training stability.

  • Sampling multiple frames per video context helps learning, with gains also appearing in reproduced MVP and R3M models.The authors report sampling at least 5 frames per clip per epoch and connect this design to improved performance on EgoSoup.
  • Generative language modeling with α=0.5 balances reconstruction and language losses, whereas larger α values can degrade control performance.The authors caution that these preliminary findings used limited pretraining duration and that α=1 may combine multiple failure modes.
  • Language descriptions narrow ambiguous visual possibilities, allowing representations to focus on task-relevant low-level details.The discussion links this focus to features useful for grasp affordance prediction, localization, and detection.
  • Language-conditioning significantly lowers reconstruction loss and enables stronger low-level feature learning, even for tasks without language input.The appendix attributes this effect to a refocusing of the learned features during pretraining.
  • Frozen-representation evaluation follows prior work and is intended to isolate the relative impact of learned visual representations.
  • Voltron requires video-language pairs, while other image and image-caption datasets are identified as possible additional data sources.
  • Transformer architecture modifications do not change downstream performance but significantly increase pretraining stability.The modifications include RMSNorm, SwishGLU, and LayerScale in place of standard components.

C.3 Results: Adroit Visuomotor Control

The Adroit evaluation tests visual representations on dexterous ball-relocation and pen-reorientation tasks with a 24-DoF robotic hand. Unlike Franka Kitchen, these clean-background tasks favor higher-level features and show that representation preferences vary by environment.

  • The Adroit benchmark evaluates 24-DoF hand control for relocating a ball and reorienting a pen.
  • V – Gen and R-R3M are most performant on Adroit, unlike Franka Kitchen, which favors V – Cond and V – Dual.
  • Clean backgrounds and individual objects make high-level progress features more important for Adroit tasks.The cited examples include whether the ball is approaching its target location.
  • The authors propose profiling more common visuomotor tasks to predict which visual features will be useful for each task.

D Data-Equivalent Reproductions & Reproducibility

The paper provides additional discussion of preprocessing and feature extraction choices affecting representation evaluation. It reports that repeated multi-frame contexts and multiheaded attention pooling improve downstream usefulness, while some explanations remain speculative.

  • D.1 Additional Preprocessing Discussion: Sampling five frames per video clip repeatedly exposes models to multiple views and finer-grained changes within the same visual context.The authors posit that this helps reconstruction capture finer-grained features.
  • D.1 Additional Preprocessing Discussion: The authors offer a speculative explanation for Voltron outperforming higher-capacity MVP models trained on substantially more data, requiring further experiments to verify.They suggest maximizing use of available pretraining data meanwhile.
  • D.2 Multiheaded Attention Pooling – Extracting Representations: MVP’s dummy <CLS> embedding receives no explicit supervision in masked autoencoding, so its captured information is not guaranteed to summarize all useful features.This motivates evaluating alternative patch-embedding pooling methods.
  • D.2 Multiheaded Attention Pooling – Extracting Representations: At n=10 Franka Kitchen demonstrations, multiheaded attention pooling strictly outperforms all evaluated extraction strategies, while mean-pooling also beats the MVP <CLS> embedding.The result motivates using MAP throughout the work.
  • D.2 Multiheaded Attention Pooling – Extracting Representations: Multiheaded attention pooling uses learned queries to cross-attend over Vision Transformer patch embeddings, producing adaptation-specific weighted combinations.This provides a feature-extraction strategy distinct from using a frozen <CLS> embedding.

E Adapting Representations for Evaluation

The evaluation adapts representations with simple task-specific heads across grasping, referring expressions, control, language-conditioned imitation, and zero-shot intent scoring. The procedures combine visual features with language or robot state where required.

  • Grasp Affordance Prediction: Grasp affordance prediction reshapes MAP-extracted patch features into a grid and uses progressive upsampling blocks to produce per-pixel label distributions.The labels are graspable, non-graspable, and background.
  • Referring Expression Grounding: Referring expression grounding extracts one dense visual representation with a single-seed MAP block and combines it with the appropriate language embedding.The language embedding differs by representation model when language conditioning is absent.
  • Single-Task Visuomotor Control: Single-task visuomotor control concatenates a MAP visual representation with proprioceptive state before BatchNorm and a two-layer MLP predicts action targets.The procedure follows the evaluation setup described in the R3M work.
  • Real-World Language-Conditioned Imitation: Real-world language-conditioned imitation similarly fuses MAP-extracted visual features and language embeddings with proprioceptive state before adaptation.The fused representation is passed through BatchNorm in the described pipeline.
  • Qualitative: Zero-Shot Intent Scoring: Zero-shot intent scoring uses no adaptation data and evaluates models capable of scoring joint vision-language contexts on sampled WHiRL video frames.The evaluated models are V – Gen, CLIP (ViT-Base), and R3M (Ego4D).
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