BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
Recently, leveraging pre-trained vision-language models (VLMs) for building
vision-language-action (VLA) models has emerged as a promising approach to
effective robot manipulation learning. However, only few methods incorporate 3D
signals into VLMs for action prediction, and they do not fully leverage the
spatial structure inherent in 3D data, leading to low sample efficiency. In
this paper, we introduce BridgeVLA, a novel 3D VLA model that (1) projects 3D
inputs to multiple 2D images, ensuring input alignment with the VLM backbone,
and (2) utilizes 2D heatmaps for action prediction, unifying the input and
output spaces within a consistent 2D image space. In addition, we propose a
scalable pre-training method that equips the VLM backbone with the capability
to predict 2D heatmaps before downstream policy learning. Extensive experiments
show the proposed method is able to learn 3D manipulation efficiently and
effectively. BridgeVLA outperforms state-of-the-art baseline methods across
three simulation benchmarks. In RLBench, it improves the average success rate
from 81.4% to 88.2%. In COLOSSEUM, it demonstrates significantly better
performance in challenging generalization settings, boosting the average
success rate from 56.7% to 64.0%. In GemBench, it surpasses all the comparing
baseline methods in terms of average success rate. In real-robot experiments,
BridgeVLA outperforms a state-of-the-art baseline method by 32% on average. It
generalizes robustly in multiple out-of-distribution settings, including visual
disturbances and unseen instructions. Remarkably, it is able to achieve a
success rate of 96.8% on 10+ tasks with only 3 trajectories per task,
highlighting its extraordinary sample efficiency. Project
Website:https://bridgevla.github.io/
