Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents
This addresses the challenge of improving generalization for embodied agents in robotics by reducing reliance on expert demonstrations, though it appears incremental as it builds on prior pre-training methods.
The paper tackled the problem of erroneous vision-language associations in pre-training for embodied agents by proposing Action Temporal Coherence Learning (AcTOL), which learns ordered and continuous representations without rigid goal constraints, resulting in significantly enhanced downstream manipulation tasks with high robustness to different linguistic styles.
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.