Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in
  Robotics
Large Vision-Language Models (LVLMs) have recently shown great promise in
advancing robotics by combining embodied reasoning with robot control. A common
approach involves training on embodied reasoning tasks related to robot control
using Supervised Fine-Tuning (SFT). However, SFT datasets are often
heuristically constructed and not explicitly optimized for improving robot
control. Furthermore, SFT often leads to issues such as catastrophic forgetting
and reduced generalization performance. To address these limitations, we
introduce Robot-R1, a novel framework that leverages reinforcement learning to
enhance embodied reasoning specifically for robot control. Robot-R1 learns to
predict the next keypoint state required for task completion, conditioned on
the current scene image and environment metadata derived from expert
demonstrations. Inspired by the DeepSeek-R1 learning approach, Robot-R1 samples
reasoning-based responses and reinforces those that lead to more accurate
predictions. Our experiments show that models trained with Robot-R1 outperform
SFT methods on embodied reasoning tasks. Despite having only 7B parameters,
Robot-R1 even surpasses GPT-4o on reasoning tasks related to low-level action
control, such as spatial and primitive movement reasoning.
683fd7d79d4fb703271ad9ac
Dongyoung Kim
683fd7d79d4fb703271ad9ad
Sumin Park
683fd7d79d4fb703271ad9ae
Huiwon Jang
683fd7d79d4fb703271ad9af
Jinwoo Shin
683fd7d79d4fb703271ad9b0
Jaehyung Kim
683fd7d79d4fb703271ad9b1
Younggyo Seo
Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in
  Robotics
Large Vision-Language Models (LVLMs) have recently shown great promise in
advancing robotics by combining embodied reasoning with robot control. A common
approach involves training on embodied reasoning tasks related to robot control
using Supervised Fine-Tuning (SFT). However, SFT datasets are often
heuristically constructed and not explicitly optimized for improving robot
control. Furthermore, SFT often leads to issues such as catastrophic forgetting
and reduced generalization performance. To address these limitations, we
introduce Robot-R1, a novel framework that leverages reinforcement learning to
enhance embodied reasoning specifically for robot control. Robot-R1 learns to
predict the next keypoint state required for task completion, conditioned on
the current scene image and environment metadata derived from expert
demonstrations. Inspired by the DeepSeek-R1 learning approach, Robot-R1 samples
reasoning-based responses and reinforces those that lead to more accurate
predictions. Our experiments show that models trained with Robot-R1 outperform
SFT methods on embodied reasoning tasks. Despite having only 7B parameters,
Robot-R1 even surpasses GPT-4o on reasoning tasks related to low-level action
control, such as spatial and primitive movement reasoning.
Robot-R1, a reinforcement learning framework, enhances embodied reasoning for robotics by predicting keypoint states, outperforming supervised fine-tuning methods and even surpassing GPT-4o in low-level action control tasks.
Large Vision-Language Models (LVLMs) embodied reasoning robot control Supervised Fine-Tuning (SFT) catastrophic forgetting generalization performance reinforcement learning keypoint state scene image environment metadata expert demonstrations DeepSeek-R1 reasoning-based responses parameter-efficient fine-tuning