robot-r1-embodied-reasoning

name	robot-r1-embodied-reasoning
title	Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2506.00070
keywords	["robotics","reinforcement-learning","embodied-reasoning","vision-language-models","spatial-reasoning"]
description	Train vision-language models for robotic manipulation using RL to improve embodied reasoning about spatial relationships and movements, achieving 31% improvement on manipulation benchmarks.

Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics

Core Concept

Robot-R1 addresses a fundamental gap in robotics training: supervised fine-tuning datasets are "often heuristically constructed and not explicitly optimized for improving robot control." The framework applies reinforcement learning to teach vision-language models to reason about spatial relationships, movements, and robot state transformations—enabling embodied reasoning that directly improves manipulation performance.

The innovation reformulates robotic control as discrete QA problems, using RL to optimize reasoning about next keypoint states. This achieves 28% improvement over SFT baselines and 31% improvement on manipulation benchmarks, with a 7B model outperforming GPT-4o on spatial reasoning tasks.

Architecture Overview

• Core Training Paradigm: GRPO (Group Relative Policy Optimization) optimizes waypoint prediction through explicit reasoning
• Task Discretization: Reformulates continuous action spaces as multiple-choice QA: predicting next robot states from visual observations
• Three Complementary Objectives: Waypoint prediction, current state identification, and movement description
• Reward Combination: Format rewards (proper output structure) + correctness rewards (exact match evaluation)
• Vision-Language Base: Built on Qwen2.5-7B-VL-Instruct, encoding visual observations of robot scenes

Implementation

1. Dataset Generation: Create training data from RLBench simulation environment (50 demonstrations per task, 224×224 resolution)

# Pseudo-code for QA-based training data generation
def generate_qa_from_demonstration(demo_trajectory, task_id):
    """
    Convert robot trajectory into spatial reasoning QA pairs.
    Generates waypoint, state, and movement prediction questions.
    """
    frames = demo_trajectory['frames']
    waypoints = demo_trajectory['keypoints']

    qa_pairs = []

    # Waypoint prediction: given current frame, predict next keypoint
    for i in range(len(frames) - 1):
        question = f"Next robot state after seeing: "
        options = [waypoints[i+], random_waypoint(), random_waypoint()]
        qa_pairs.append({: question, : options})

    
     i  ((frames)):
        question = 
        options = [waypoints[i], random_waypoint(), random_waypoint()]
        qa_pairs.append({: question, : options})

    
     i  ((frames) - ):
        movement = describe_direction(waypoints[i], waypoints[i+])
        qa_pairs.append({: question, : movement})

     qa_pairs