arxiv:2609.14973
Published on Sep 14
· Submitted by
yubin on Sep 15
· DeepCybo
Upvote
171
Authors:
,
Yu Bin ,
,
,
,
,
,
,
,
,
,
Jiyan He ,
,
,
,
,
,
,
,
,
Abstract
PhysBrain 1.5 unifies physical environment understanding, action generation, and future state prediction via joint autoregressive training on discrete vision-language, motion, and visual target sequences, achieving state-of-the-art open-source embodied performance.
Generated by thinkingmachines/Inkling-Small
We present PhysBrain 1.5, a unified model for understanding physical environments, generating actions, and predicting future states. Motivated by the physical loop of observation, interaction, and environmental change, we bring these capabilities into a common learning framework. Starting from a general vision--language model, we encode language responses, end-effector motion, and dense visual targets as discrete sequences and jointly optimize them with autoregressive next-token prediction. Pre-training draws its embodied supervision entirely from human interaction videos, using task-centered episodes to pair semantic and spatial context with recovered motion and subsequent observations. We then adapt the model through supervised fine-tuning on a mixture of human demonstrations, robot trajectories, and simulated experience. Across 28 embodied understanding benchmarks, our 8B model achieves an average score of 72.5, setting a new open-source state of the art and performing on par with leading proprietary models such as GPT-6-Astra and Gemini 3.6 Flash. It achieves the best open-source results on 14 benchmarks while retaining general multimodal capabilities. Beyond these understanding evaluations, qualitative examples show the model's ability to produce end-effector trajectories and predict future scenes through spatially aligned RGB, depth, and robot-mask outputs.
View arXiv page View PDF Project pageGitHub 44 Add to collection
Community
Paper author Paper submitter 8 days ago
PhysBrain 1.5 is a unified embodied foundation model that understands the observed world, generates goal-directed actions, and predicts how the environment will evolve — all as discrete tokens under a single shared autoregressive backbone.
8 days ago
This is an automated message from the ResearchStudio team.
We created an interactive ResearchStudio Reel for this paper. It includes a visual poster, a video, and a blog, all available for download in editable formats.
Open the ResearchStudio Reel →
Download all files from HF Mirror
Please give this comment a thumbs up if you find the Reel helpful!
Want to explore or create Reels for more papers? Visit the ResearchStudio demo.
7 days ago
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
-
JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment (2026)
-
WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory (2026)
-
LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation (2026)
-
ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training (2026)
-
RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model (2026)
-
Riemann-1.0: An Embodied World Action Model for Physical AI (2026)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on HF Mirror checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend
Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.
Tap or paste here to upload images
· Sign up or log in to comment
Upvote
171
Get this paper in your agent:
hf papers read 2609.14973
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash
Models citing this paper 2
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2609.14973 in a dataset README.md to link it from this page.