Splat2Act: From Captured Scenes to Personalized VLA Manipulation
cs.RO, cs.CV
Submitted: 2026-10-09
Updated: 2026-10-09
Comments: Project page: https://splat2act.github.io
Project page: https://splat2act.github.io
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- World Action Models are Zero-shot Policies
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World
- PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies
- GSWorld: Closed-Loop Photo-Realistic Simulation Suite for Robotic Manipulation
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting
- MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
- GenSim: Generating Robotic Simulation Tasks via Large Language Models
- RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
- EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device
- Evaluating Real-World Robot Manipulation Policies in Simulation
- SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting
- Re$^3$Sim: Generating High-Fidelity Simulation Data via 3D-Photorealistic Real-to-Sim for Robotic Manipulation
- High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic Manipulation Learning with Gaussian Splatting
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