langforce-vision-language-action

name	langforce-vision-language-action
title	LangForce: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.15197
keywords	["vision-language-action","bayesian-decomposition","robotic-control","language-alignment","latent-actions"]
description	Align vision-language-action models with natural language instructions using Bayesian decomposition with latent action queries, improving robotic manipulation generalization. Use when building agents that follow language commands while observing visual scenes and producing motor control.

LangForce: Vision-Language-Action Alignment

This skill enables vision-language-action models to better follow language instructions by decomposing the problem using Bayesian inference and latent action queries, improving out-of-distribution generalization for robotic tasks.

When to Use

• Building robotic agents that follow natural language instructions
• Training vision-language-action models for manipulation tasks
• Systems where language commands should guide visual policy decisions
• Tasks requiring strong generalization to unseen instruction variations

When NOT to Use

• Pure visual navigation without language guidance
• Simple rule-based control systems (doesn't need Bayesian decomposition)
• Real-time inference with strict latency requirements
• Tasks with limited instruction variation

Key Concept

Vision-language-action (VLA) models often ignore language instructions, treating them as secondary. LangForce addresses this through Bayesian decomposition that explicitly maximizes alignment between:

• Language instructions (what the user wants)
• Visual observations (what's in the scene)
• Actions (what the robot should do)

The method uses latent action queries to explore the space of possible actions consistent with both vision and language.

Implementation Pattern

Implement Bayesian decomposition to align vision, language, and action:

# Pseudocode for LangForce alignment
class LangForce:
    def __init__(self, vla_model, latent_action_dim=64):
        self.vla = vla_model
        self.latent_action_dim = latent_action_dim

    def infer_action(self, observation, language_instruction):
        # Encode visual observation
        visual_features = self.vla.encode_vision(observation)

        # Encode language instruction
        language_features = self.vla.encode_language(language_instruction)

        
        
        latent_actions = .sample_latent_actions(k=)

        scores = []
         latent_action  latent_actions:
            
            action = .vla.decode_action(latent_action)

            
            vision_score = .score_consistency(action, visual_features)
            language_score = .score_consistency(action, language_features)

            combined_score = vision_score + language_score
            scores.append(combined_score)

        
        best_idx = argmax(scores)
         latent_actions[best_idx]