vla for contact rich manipulation

The paper directly addresses the topic by proposing a framework specifically designed to adapt VLA models for contact-rich manipulation.

The paper directly proposes a Vision-Language-Action (VLA) model specifically designed to enhance contact-rich manipulation by incorporating audio perception.

The paper explicitly focuses on developing a Vision-Language-Action (VLA) model specifically for contact-rich manipulation.

The paper directly proposes a method to enhance VLA models specifically for contact-rich robotic manipulation tasks using Force/Torque integration.

The paper directly addresses the development of an end-to-end VLA policy for dexterous arm-hand manipulation using tactile feedback and force-adaptive actions, which is a core example of contact-rich manipulation.

The paper explicitly focuses on a Multisensory Language-Action model designed specifically to improve complex and contact-rich robotic manipulation by integrating tactile tokens and other sensory modalities.

The paper presents a VLA system specifically designed for high-DoF bimanual dexterity, which is inherently centered on contact-rich manipulation.

The paper explicitly proposes a Vision-Language-Action (VLA) model for surgical debulking, which inherently involves contact-rich manipulation tasks such as grasping and transferring.

The paper directly addresses contact-rich manipulation by leveraging Vision-Language Models (VLMs) to determine acceptable contacts and guide motion planning.

The paper directly addresses Vision-Language-Action (VLA) models for various manipulation tasks across different embodiments, which is central to contact-rich manipulation.

The paper presents a VLA framework that explicitly addresses the need for precise manipulation by separating gross arm movement from fine gripper control, which is fundamental for contact-rich tasks.

The paper proposes a VLA model integrating non-RGB sensors (radar, infrared, audio) which are critical for overcoming visual occlusions and perceiving physical interactions typical of contact-rich manipulation.

The paper directly addresses contact-rich manipulation using a multimodal transformer policy combining vision and force data, although it focuses on Vision-Force-Action rather than incorporating Language.

The paper focuses on improving the spatial understanding of VLA models for precise robotic manipulation across benchmarks like LIBERO, which is highly relevant to the requirements of contact-rich tasks.

The paper focuses on improving VLA models' spatial understanding via depth augmentation, which is critical for precise robotic manipulation, although it does not explicitly address the tactile or force-feedback aspects typical of 'contact-rich' tasks.

The paper presents a comprehensive VLA platform and benchmark for humanoid robots, which are central to contact-rich manipulation, though it focuses more on the infrastructure and Sim2Real transfer than specific contact-rich techniques.

The paper proposes a VLA framework for robotic manipulation using generative VLMs, but it focuses more on high-level goal generation and spatial reasoning than specifically on contact-rich dynamics.

The paper focuses on improving VLA performance for long-horizon general robotic manipulation via MCTS and reasoning, but does not explicitly address the specific challenges of contact-rich manipulation such as force control or tactile feedback.

While the paper focuses on improving the efficiency of VLA models used in robotic manipulation, it addresses computational overhead rather than the specific physical or control challenges associated with contact-rich manipulation.

While the paper focuses on VLA models for manipulation and HRI, it emphasizes high-level intention reasoning rather than the low-level physics or control challenges specific to contact-rich manipulation.

While the paper discusses Vision-Language-Action (VLA) models for robotic manipulation, it focuses on hardware affordability and general task success rather than the specific challenges of contact-rich manipulation.

The paper focuses on a hardware sensor for contact-rich manipulation but does not mention or utilize Vision-Language-Action (VLA) models.

While the paper discusses Vision-Language-Action (VLA) models in robotics, its focus is on adversarial security attacks rather than the mechanics or implementation of contact-rich manipulation.

While the paper addresses contact-rich manipulation via soft robotics and sensing, it does not involve Vision-Language-Action (VLA) models or AI-driven policy learning.

The paper focuses on hardware materials for variable stiffness and morphological control, whereas the topic of interest is high-level Vision-Language-Action (VLA) models.