BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning
  with Vision-Language Models
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/
684eae5e60b4a34dbe0079ea
Peiyan Li
claimed_verified
2025-06-16T07:16:27.694Z
6337e04b171879571956212f
/avatars/aa69142bf92e4c50b192463490251ed9.svg
Li Peiyan
user
LPY
684eae5e60b4a34dbe0079eb
Yixiang Chen
684eae5e60b4a34dbe0079ec
Hongtao Wu
684eae5e60b4a34dbe0079ed
Xiao Ma
claimed_verified
2025-07-24T09:13:49.769Z
6478597d91398856110a6738
/avatars/c3bc61eb7554ac21946b424e1314f1a7.svg
Xiao Ma
user
yusufma555
684eae5e60b4a34dbe0079ee
Xiangnan Wu
684eae5e60b4a34dbe0079ef
Yan Huang
684eae5e60b4a34dbe0079f0
Liang Wang
684eae5e60b4a34dbe0079f1
Tao Kong
684eae5e60b4a34dbe0079f2
Tieniu Tan
BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning
  with Vision-Language Models
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/
BridgeVLA is a 3D vision-language-action model that projects 3D inputs to 2D images and uses 2D heatmaps for efficient and effective action prediction, outperforming baselines in various benchmarks.
pre-trained vision-language models 3D VLA model 3D signals 2D images VLM backbone 2D heatmaps scalable pre-training method