TL;DR
PRTS introduces a goal-conditioned reinforcement learning approach to vision-language-action models, enabling better temporal reasoning and goal reachability understanding for robotic control.
Contribution
It reformulates pretraining as goal-conditioned reinforcement learning with contrastive embeddings, improving reasoning and planning in robotic models.
Findings
State-of-the-art performance on multiple benchmarks.
Significant gains in long-horizon and zero-shot tasks.
Enhanced goal reachability understanding improves success rates.
Abstract
Vision-Language-Action (VLA) models advance robotic control via strong visual-linguistic priors. However, existing VLAs predominantly frame pretraining as supervised behavior cloning, overlooking the fundamental nature of robot learning as a goal-reaching process that requires understanding temporal task progress. We present \textbf{PRTS} (\textbf{P}rimitive \textbf{R}easoning and \textbf{T}asking \textbf{S}ystem), a VLA foundation model that reformulates pretraining through Goal-Conditioned Reinforcement Learning. By treating language instructions as goals and employing contrastive reinforcement learning, PRTS learns a unified embedding space where the inner product of state-action and goal embeddings approximates the log-discounted goal occupancy, the probability of reaching the language-specified goal from the current state-action, quantitatively assessing physical feasibility beyond…
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