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Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents

Zhizhen Zhang, Lei Zhu, Zhen Fang, Zi Huang, Yadan Luo

TL;DR

This work tackles the problem of learning robust vision-language representations for embodied agents from noisy human action videos. It introduces AcTOL, combining a Vision-Language Ordering loss with a Brownian-bridge continuity constraint to capture the natural ordering and smooth transitions of actions without relying on rigid goal frames. Theoretical guarantees on ordering and continuity are provided, along with extensive experiments on real and simulated robots showing improved imitation learning performance, robustness to linguistic perturbations, and the ability to generate semantically meaningful dense rewards. The approach demonstrates strong data efficiency and generalization potential for generalized embodied intelligence, including adaptability via fine-tuning with limited in-domain data.

Abstract

Pre-training vision-language representations on human action videos has emerged as a promising approach to reduce reliance on large-scale expert demonstrations for training embodied agents. However, prior methods often employ time contrastive learning based on goal-reaching heuristics, progressively aligning language instructions from the initial to the final frame. This overemphasis on future frames can result in erroneous vision-language associations, as actions may terminate early or include irrelevant moments in the end. To address this issue, we propose Action Temporal Coherence Learning (AcTOL) to learn ordered and continuous vision-language representations without rigid goal-based constraint. AcTOL treats a video as a continuous trajectory where it (1) contrasts semantic differences between frames to reflect their natural ordering, and (2) imposes a local Brownian bridge constraint to ensure smooth transitions across intermediate frames. Extensive imitation learning experiments on both simulated and real robots show that the pretrained features significantly enhance downstream manipulation tasks with high robustness to different linguistic styles of instructions, offering a viable pathway toward generalized embodied agents.

Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents

TL;DR

This work tackles the problem of learning robust vision-language representations for embodied agents from noisy human action videos. It introduces AcTOL, combining a Vision-Language Ordering loss with a Brownian-bridge continuity constraint to capture the natural ordering and smooth transitions of actions without relying on rigid goal frames. Theoretical guarantees on ordering and continuity are provided, along with extensive experiments on real and simulated robots showing improved imitation learning performance, robustness to linguistic perturbations, and the ability to generate semantically meaningful dense rewards. The approach demonstrates strong data efficiency and generalization potential for generalized embodied intelligence, including adaptability via fine-tuning with limited in-domain data.

Abstract

Pre-training vision-language representations on human action videos has emerged as a promising approach to reduce reliance on large-scale expert demonstrations for training embodied agents. However, prior methods often employ time contrastive learning based on goal-reaching heuristics, progressively aligning language instructions from the initial to the final frame. This overemphasis on future frames can result in erroneous vision-language associations, as actions may terminate early or include irrelevant moments in the end. To address this issue, we propose Action Temporal Coherence Learning (AcTOL) to learn ordered and continuous vision-language representations without rigid goal-based constraint. AcTOL treats a video as a continuous trajectory where it (1) contrasts semantic differences between frames to reflect their natural ordering, and (2) imposes a local Brownian bridge constraint to ensure smooth transitions across intermediate frames. Extensive imitation learning experiments on both simulated and real robots show that the pretrained features significantly enhance downstream manipulation tasks with high robustness to different linguistic styles of instructions, offering a viable pathway toward generalized embodied agents.

Paper Structure

This paper contains 31 sections, 3 theorems, 58 equations, 12 figures, 14 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Vision-language Pre-training.
  4. Our Approach: AcTOL
  5. Visual-Language Ordering
  6. Vision-Language Continuity
  7. Theoretical Analysis
  8. Experiment
  9. Language-Conditioned Behavior Cloning
  10. Language-Conditioned Visual Rewards
  11. Robustness Study under Linguistic Perturbations
  12. Mitigating the Human-to-Robot Gap via Fine-Tuning
  13. Related Work
  14. Conclusion and Limitations
  15. Pre-training Details
  16. ...and 16 more sections

Key Result

Theorem 1

$\mathcal{L}^*$ is a tight lower bound of $\mathcal{L}_{\mathrm{VLO}}$, i.e., $\mathcal{L}_{\mathrm{VLO}} \geq \mathcal{L}^*$, and for any $\epsilon > 0$, there exists feature embeddings such that $\mathcal{L}_{\mathrm{VLO}} < \mathcal{L}^* + \epsilon$. Furthermore, for any $0 < \delta < 1$, there e

Figures (12)

  • Figure 1: Pretraining on Internet human action videos for robot control, where the video-instruction pairs are noisy and often include irrelevant frames. The red vision-language reward curve demonstrates AcTOL learns to correctly align instruction with action, outperforming previous goal-reaching methods in the presence of distracting content.
  • Figure 2: Comparison of existing goal-reaching pre-training strategies and the proposed AcTOL approach. Our learned multi-modal representations can be effectively transferred to downstream language-conditioned robot manipulation tasks, exhibiting robustness to diverse instruction and linguistic variations.
  • Figure 3: Policy learning environments, including 3 tasks with a real-world Unitree D1 robot arm and 5 tasks each in two simulation environments, i.e., Franka Kitchen and Metaworld.
  • Figure 3: Performance comparison on Unitree D1 arm. Success rates are reported over 10 trials.
  • Figure 4: Visual shifts applied in Franka Kitchen.
  • ...and 7 more figures

Theorems & Definitions (4)

  • Definition 1: VLO Representations
  • Theorem 1: Vision-Language Ordering
  • Theorem 2: Vision-Language Continuity
  • Theorem 3: Robustness to Language Variations