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Latent Action Learning Requires Supervision in the Presence of Distractors
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Recently, latent action learning, pioneered by Latent Action Policies (LAPO), have shown remarkable pre-training efficiency on observation-only data, offering potential for leveraging vast amounts of video available on the web for embodied AI. However, prior work has focused on distractor-free data, where changes between observations are primarily explained by ground-truth actions. Unfortunately, real-world videos contain action-correlated distractors that may hinder latent action learning. Using Distracting Control Suite (DCS) we empirically investigate the effect of distractors on latent action learning and demonstrate that LAPO struggle in such scenario. We propose LAOM, a simple LAPO modification that improves the quality of latent actions by 8x, as measured by linear probing. Importantly, we show that providing supervision with ground-truth actions, as few as 2.5% of the full dataset, during latent action learning improves downstream performance by 4.2x on average. Our findings suggest that integrating supervision during Latent Action Models (LAM) training is critical in the presence of distractors, challenging the conventional pipeline of first learning LAM and only then decoding from latent to ground-truth actions.
Forward citations
Cited by 10 Pith papers
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LAVIFT: Latent-Action-Guided Vision Fine-Tuning for Surgical Interaction Recognition
Latent-action modeling (inverse dynamics plus forward world model) with a patch-level anti-collapse regularizer improves surgical action-triplet recognition and makes encoder change features land more on instrument-ti...
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Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning
Open-AoE releases 2,000 hours of smartphone egocentric manipulation video with MANO hand poses, camera trajectories, atomic action labels, and training tools for VLA and world-model pipelines.
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PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
PoLAR imposes radial structure on latent actions in hyperbolic space to factorize extent and mode, improving robot policy performance over baselines.
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LARA: Latent Action Representation Alignment for Vision-Language-Action Models
LARA jointly optimizes LAM and VLA models via representation alignment to improve robotic manipulation performance using human videos.
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CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization
CLAW is an end-to-end self-supervised method that learns semantically meaningful continuous latent actions and predictive world models from action-free videos to support imitation learning and goal-directed planning.
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From Pixels to Tokens: A Systematic Study of Latent Action Supervision for Vision-Language-Action Models
A unified comparison of latent action supervision strategies for VLA models reveals task-specific benefits, with image-based approaches aiding reasoning and generalization, action-based aiding motor control, and discr...
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Factored Latent Action World Models
FLAM splits a scene into separate factors, each with its own latent action, and reports better video prediction and downstream policy learning than monolithic latent-action models.
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villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models
villa-X enhances latent action modeling in VLA models to support zero-shot action planning for unseen robot embodiments and open-vocabulary instructions, yielding better manipulation results in simulation and real-wor...
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LARA: Latent Action Representation Alignment for Vision-Language-Action Models
LARA jointly optimizes LAM and VLA models via representation alignment, reporting average gains of ~10%, ~5%, and ~15% on simulation and real robotic manipulation tasks.
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Motus: A Unified Latent Action World Model
Motus unifies understanding, video generation, and action in one latent world model via MoT experts and optical-flow latent actions, reporting gains over prior methods in simulation and real robots.
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