GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs

• URL: http://arxiv.org/abs/2410.03645v1
• Date: Fri, 4 Oct 2024 17:51:33 GMT
• Title: GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs
• Authors: Pu Hua, Minghuan Liu, Annabella Macaluso, Yunfeng Lin, Weinan Zhang, Huazhe Xu, Lirui Wang,
• Abstract summary: GenSim2 is a scalable framework for complex and realistic simulation task creation. The pipeline can generate data for up to 100 articulated tasks with 200 objects and reduce the required human efforts. We show a promising usage of GenSim2 that the generated data can be used for zero-shot transfer or co-train with real-world collected data.
• Score: 38.281562732050084
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scalability issues as many sim-to-real methods focus on a single task. To address these challenges, this work proposes GenSim2, a scalable framework that leverages coding LLMs with multi-modal and reasoning capabilities for complex and realistic simulation task creation, including long-horizon tasks with articulated objects. To automatically generate demonstration data for these tasks at scale, we propose planning and RL solvers that generalize within object categories. The pipeline can generate data for up to 100 articulated tasks with 200 objects and reduce the required human efforts. To utilize such data, we propose an effective multi-task language-conditioned policy architecture, dubbed proprioceptive point-cloud transformer (PPT), that learns from the generated demonstrations and exhibits strong sim-to-real zero-shot transfer. Combining the proposed pipeline and the policy architecture, we show a promising usage of GenSim2 that the generated data can be used for zero-shot transfer or co-train with real-world collected data, which enhances the policy performance by 20% compared with training exclusively on limited real data.

Related papers

• GRS: Generating Robotic Simulation Tasks from Real-World Images [18.64560477109255]
  GRS (Generating Robotic Simulation tasks) is a novel system to address the challenge of real-to-sim in robotics, computer vision, and AR/VR. We use state-of-the-art vision-language models (VLMs) to achieve a comprehensive real-to-sim pipeline. GRS operates in three stages: 1) scene comprehension using SAM2 for object segmentation, 2) matching identified objects with simulation-ready assets, and 3) generating contextually appropriate robotic tasks.
  arXiv  Detail & Related papers  (2024-10-20T23:33:06Z)
• Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation [48.17611255751571]
  Post-training is essential for enabling large language models to follow human instructions. We leverage multi-agent simulation to automatically generate diverse text-based scenarios. We introduce a novel scenario-driven instruction generator MATRIX-Gen for controllable and highly realistic data synthesis.
  arXiv  Detail & Related papers  (2024-10-18T08:01:39Z)
• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• GenSim: Generating Robotic Simulation Tasks via Large Language Models [34.79613485106202]
  GenSim aims to automatically generate rich simulation environments and expert demonstrations. We use GPT4 to expand the existing benchmark by ten times to over 100 tasks. With minimal sim-to-real adaptation, multitask policies pretrained on GPT4-generated simulation tasks exhibit stronger transfer to unseen long-horizon tasks in the real world.
  arXiv  Detail & Related papers  (2023-10-02T17:23:48Z)
• Reactive Long Horizon Task Execution via Visual Skill and Precondition Models [59.76233967614774]
  We describe an approach for sim-to-real training that can accomplish unseen robotic tasks using models learned in simulation to ground components of a simple task planner. We show an increase in success rate from 91.6% to 98% in simulation and from 10% to 80% success rate in the real-world as compared with naive baselines.
  arXiv  Detail & Related papers  (2020-11-17T15:24:01Z)
• RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real [74.45688231140689]
  We introduce the RL-scene consistency loss for image translation, which ensures that the translation operation is invariant with respect to the Q-values associated with the image. We obtain RL-CycleGAN, a new approach for simulation-to-real-world transfer for reinforcement learning.
  arXiv  Detail & Related papers  (2020-06-16T08:58:07Z)

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.