GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped Robot

• URL: http://arxiv.org/abs/2403.13358v2
• Date: Tue, 9 Apr 2024 07:55:41 GMT
• Title: GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped Robot
• Authors: Wenxuan Song, Han Zhao, Pengxiang Ding, Can Cui, Shangke Lyu, Yaning Fan, Donglin Wang,
• Abstract summary: In this paper, we propose GeRM (Generalist Robotic Model) We utilize offline reinforcement learning to optimize data utilization strategies. We employ a transformer-based VLA network to process multi-modal inputs and output actions.
• Score: 27.410618312830497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-task robot learning holds significant importance in tackling diverse and complex scenarios. However, current approaches are hindered by performance issues and difficulties in collecting training datasets. In this paper, we propose GeRM (Generalist Robotic Model). We utilize offline reinforcement learning to optimize data utilization strategies to learn from both demonstrations and sub-optimal data, thus surpassing the limitations of human demonstrations. Thereafter, we employ a transformer-based VLA network to process multi-modal inputs and output actions. By introducing the Mixture-of-Experts structure, GeRM allows faster inference speed with higher whole model capacity, and thus resolves the issue of limited RL parameters, enhancing model performance in multi-task learning while controlling computational costs. Through a series of experiments, we demonstrate that GeRM outperforms other methods across all tasks, while also validating its efficiency in both training and inference processes. Additionally, we uncover its potential to acquire emergent skills. Additionally, we contribute the QUARD-Auto dataset, collected automatically to support our training approach and foster advancements in multi-task quadruped robot learning. This work presents a new paradigm for reducing the cost of collecting robot data and driving progress in the multi-task learning community. You can reach our project and video through the link: https://songwxuan.github.io/GeRM/ .

Related papers

• Robo-MUTUAL: Robotic Multimodal Task Specification via Unimodal Learning [35.42091835421386]
  Multimodal task specification is essential for enhanced robotic performance. We show that by leveraging unimodal instructions abundant in real data, we can effectively teach robots to learn multimodal task specifications.
  arXiv  Detail & Related papers  (2024-10-02T13:23:02Z)
• VITAL: Visual Teleoperation to Enhance Robot Learning through Human-in-the-Loop Corrections [10.49712834719005]
  We propose a low-cost visual teleoperation system for bimanual manipulation tasks, called VITAL. Our approach leverages affordable hardware and visual processing techniques to collect demonstrations. We enhance the generalizability and robustness of the learned policies by utilizing both real and simulated environments.
  arXiv  Detail & Related papers  (2024-07-30T23:29:47Z)
• Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation [8.940998315746684]
  We propose a model-based reinforcement learning (RL) approach for robotic arm end-tasks. We employ Bayesian neural network models to represent, in a probabilistic way, both the belief and information encoded in the dynamic model during exploration. Our experiments show the advantages of our Bayesian model-based RL approach, with similar quality in the results than relevant alternatives.
  arXiv  Detail & Related papers  (2024-04-02T11:44:37Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning [49.92517970237088]
  We tackle the problem of training a robot to understand multimodal prompts. This type of task poses a major challenge to robots' capability to understand the interconnection and complementarity between vision and language signals. We introduce an effective framework that learns a policy to perform robot manipulation with multimodal prompts.
  arXiv  Detail & Related papers  (2023-10-14T22:24:58Z)
• REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation [61.7171775202833]
  We introduce an efficient system for learning dexterous manipulation skills withReinforcement learning. The main idea of our approach is the integration of recent advances in sample-efficient RL and replay buffer bootstrapping. Our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy.
  arXiv  Detail & Related papers  (2023-09-06T19:05:31Z)
• Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning [61.3506230781327]
  In robotics, one approach to generate training data builds on simulations based on dynamics models derived from first principles. Here, we leverage the imbalance in complexity of the dynamics to learn more sample-efficiently. We validate our method on several challenging simulated tasks and demonstrate that it improves learning both alone and when combined with an existing hindsight algorithm.
  arXiv  Detail & Related papers  (2023-03-03T21:55:04Z)
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
  Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems. We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
  arXiv  Detail & Related papers  (2021-09-19T18:00:51Z)
• Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention [67.1936055742498]
  We show that multi-task learning can effectively scale reset-free learning schemes to much more complex problems. This work shows the ability to learn dexterous manipulation behaviors in the real world with RL without any human intervention.
  arXiv  Detail & Related papers  (2021-04-22T17:38:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.