Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation
cs.RO
Submitted: 2026-09-28
Updated: 2026-09-30
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- Octo: An Open-Source Generalist Robot Policy
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration
- SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
- WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control
- OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
- Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding
- WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning
- Learning Versatile Humanoid Manipulation with Touch Dreaming
- RT-1: Robotics Transformer for Real-World Control at Scale
- PaLM-E: An Embodied Multimodal Language Model
- RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
- Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving