BridgeVLA++

Pre-training data, checkpoints and benchmark keyframe data for BridgeVLA++ and its predecessor BridgeVLA.

BridgeVLA++ is a 3D vision-language-action framework that preserves the input-output alignment of a pre-trained VLM during 3D action learning — point clouds are projected into multi-view images and intermediate heatmaps are predicted before actions — and extends it with a unified spatio-temporal memory modeling persistent spatial context and temporal interaction history. It matches or surpasses BridgeVLA on the original benchmarks without sacrificing data efficiency or generalization, reaches state of the art on two memory-dependent benchmarks, and extends to bimanual manipulation and a new real-world embodiment.

Contents

checkpoints/
├── pretrain/              # grounding pre-training weights, shared by both models (finetune warm-start)
├── bridgevla/             # BridgeVLA (original)
│   └── rlbench/ colosseum/ gembench/
└── bridgevla_plus/        # BridgeVLA++ weights
    ├── rlbench/ colosseum/ gembench/ memorybench/
    └── rmbench/<task>/    # per-task, 9 tasks
pretrain_data/
├── coco.tar.gz            # COCO images
└── detection_data.json    # RoboPoint grounding annotations
datasets/                  # benchmark keyframe data (see below)
├── rlbench/keyframe_cache/size128_v2/<task>/episode<N>.npz + .npz.meta
├── memorybench/keyframe_cache/size128_v3/<task>/episode<N>.npz + .npz.meta
└── rmbench/
    ├── keyframe_data/<task>/keyframe_depth/   # keyframe-only HDF5 training data
    └── keyframes/<task>.json                  # keyframe metadata -> memory labels

Benchmark keyframe data

datasets/ ships the precomputed keyframe artifacts training depends on — do not rearrange them by hand; the code repo's scripts/download_checkpoints_hf.sh (or scripts/download_checkpoints_ms.sh for the identical ModelScope mirror) with a target of rlbench_cache / memorybench_cache / rmbench_data (or per-task rmbench_data:<task>) places each into the exact layout the trainers expect:

• rlbench / memorybench keyframe_cache — pre-built episode caches (.npz decoded observations + .meta canonical keyframe indices). The .meta files pin RLBench's per-(task, variation) majority-vote canonical keyframes: shipping them makes training runs reproduce ours exactly, and skips a multi-hour local build. RLBench training requires this cache.
• rmbench keyframe_data + keyframes — the actual RMBench training set (keyframe-only re-rendered HDF5, 50 episodes x 10 tasks) plus per-keyframe metadata whose language_annotation/subtask_idx provide the memory supervision labels. With these, the raw 37 GiB demo_clean demos are NOT needed for training or evaluation. The two directories must stay siblings.

If you download this repo manually instead (e.g. huggingface-cli download / modelscope download of the whole repo), place each tree as below. The local names differ from the repo paths on purpose (_keyframe_cache has a leading underscore; capitalization and nesting differ too), so copying datasets/ into the code repo as-is will NOT be found by the trainers:

in this repo	local path in the code repo
checkpoints/	data/bridgevla_ckpt/ (inner layout unchanged)
pretrain_data/	data/bridgevla_data/pretrain_data/
datasets/rlbench/keyframe_cache/	data/bridgevla_data/RLBench/_keyframe_cache/
datasets/memorybench/keyframe_cache/	data/bridgevla_data/memorybench/data/train/_keyframe_cache/
datasets/rmbench/keyframe_data/	data/bridgevla_data/RMBench/data/keyframe_data/
datasets/rmbench/keyframes/	data/bridgevla_data/RMBench/data/keyframes/

A misplaced path never degrades silently: training fails fast with an error naming the expected location and the download command that fills it.

Every checkpoint directory holds model_<epoch>.pth together with exp_cfg.yaml and mvt_cfg.yaml; the two configs define the network architecture and must stay next to the weights.

epoch	RLBench	COLOSSEUM	GemBench	memoryBench	RMBench
BridgeVLA++	130	200	200	160	per-task
BridgeVLA	80	80	40	–	–

The bridgevla/ checkpoints belong to the original BridgeVLA codebase and are not loadable by BridgeVLA++; run them with that codebase. Only pretrain/ is shared by both.

Papers

• BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation — arXiv coming soon.
• BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models — arXiv:2506.07961

Usage instructions live in the code repository.

Provenance

pretrain/, bridgevla/ and pretrain_data/ are the artifacts released with BridgeVLA. pretrain_data/ builds on COCO images and RoboPoint-style grounding annotations, which remain subject to their original terms; the Apache-2.0 license above applies to the model weights.

datasets/ is derived data: the rlbench cache from the PerAct RLBench demos (hqfang/rlbench-18-tasks), the memorybench cache from SAM2Act's MemoryBench data (hqfang/memorybench), and the rmbench trees re-rendered from RoboTwin 2.0 / RMBench (TianxingChen/RMBench). Each remains subject to its upstream benchmark's terms.