RH20T-P: A Primitive-Level Robotic Dataset Towards Composable Generalization Agents

• URL: http://arxiv.org/abs/2403.19622v1
• Date: Thu, 28 Mar 2024 17:42:54 GMT
• Title: RH20T-P: A Primitive-Level Robotic Dataset Towards Composable Generalization Agents
• Authors: Zeren Chen, Zhelun Shi, Xiaoya Lu, Lehan He, Sucheng Qian, Hao Shu Fang, Zhenfei Yin, Wanli Ouyang, Jing Shao, Yu Qiao, Cewu Lu, Lu Sheng,
• Abstract summary: The ultimate goals of robotic learning is to acquire a comprehensive and generalizable robotic system. Recent progress in utilizing language models as high-level planners has demonstrated that the complexity of tasks can be reduced through decomposing them into primitive-level plans. Despite the promising future, the community is not yet adequately prepared for composable generalization agents.
• Score: 107.97394661147102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ultimate goals of robotic learning is to acquire a comprehensive and generalizable robotic system capable of performing both seen skills within the training distribution and unseen skills in novel environments. Recent progress in utilizing language models as high-level planners has demonstrated that the complexity of tasks can be reduced through decomposing them into primitive-level plans, making it possible to generalize on novel robotic tasks in a composable manner. Despite the promising future, the community is not yet adequately prepared for composable generalization agents, particularly due to the lack of primitive-level real-world robotic datasets. In this paper, we propose a primitive-level robotic dataset, namely RH20T-P, which contains about 33000 video clips covering 44 diverse and complicated robotic tasks. Each clip is manually annotated according to a set of meticulously designed primitive skills, facilitating the future development of composable generalization agents. To validate the effectiveness of RH20T-P, we also construct a potential and scalable agent based on RH20T-P, called RA-P. Equipped with two planners specialized in task decomposition and motion planning, RA-P can adapt to novel physical skills through composable generalization. Our website and videos can be found at https://sites.google.com/view/rh20t-primitive/main. Dataset and code will be made available soon.

Related papers

• SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model [45.03115608632622]
  spatial understanding is the keypoint in robot manipulation. We propose SpatialVLA to explore effective spatial representations for the robot foundation model. We show the proposed Adaptive Action Grids offer a new and effective way to fine-tune the pre-trained SpatialVLA model for new simulation and real-world setups.
  arXiv  Detail & Related papers  (2025-01-27T07:34:33Z)
• GRAPE: Generalizing Robot Policy via Preference Alignment [58.419992317452376]
  We present GRAPE: Generalizing Robot Policy via Preference Alignment. We show GRAPE increases success rates on in-domain and unseen manipulation tasks by 51.79% and 58.20%, respectively. GRAPE can be aligned with various objectives, such as safety and efficiency, reducing collision rates by 37.44% and rollout step-length by 11.15%, respectively.
  arXiv  Detail & Related papers  (2024-11-28T18:30:10Z)
• Robotic Control via Embodied Chain-of-Thought Reasoning [86.6680905262442]
  Key limitation of learned robot control policies is their inability to generalize outside their training data. Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models can substantially improve their robustness and generalization ability. We introduce Embodied Chain-of-Thought Reasoning (ECoT) for VLAs, in which we train VLAs to perform multiple steps of reasoning about plans, sub-tasks, motions, and visually grounded features before predicting the robot action.
  arXiv  Detail & Related papers  (2024-07-11T17:31:01Z)
• RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking [54.776890150458385]
  We develop an efficient system for training universal agents capable of multi-task manipulation skills. We are able to train a single agent capable of 12 unique skills, and demonstrate its generalization over 38 tasks. On average, RoboAgent outperforms prior methods by over 40% in unseen situations.
  arXiv  Detail & Related papers  (2023-09-05T03:14:39Z)
• RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot [56.130215236125224]
  A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations. This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception.
  arXiv  Detail & Related papers  (2023-07-02T15:33:31Z)
• Continual Vision-based Reinforcement Learning with Group Symmetries [18.7526848176769]
  We introduce a unique Continual Vision-based Reinforcement Learning method that recognizes Group Symmetries, called COVERS. Our results show that COVERS accurately assigns tasks to their respective groups and significantly outperforms existing methods in terms of generalization capability.
  arXiv  Detail & Related papers  (2022-10-21T23:41:02Z)
• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning [108.41464483878683]
  We study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks. We develop an interactive and flexible imitation learning system that can learn from both demonstrations and interventions. When scaling data collection on a real robot to more than 100 distinct tasks, we find that this system can perform 24 unseen manipulation tasks with an average success rate of 44%.
  arXiv  Detail & Related papers  (2022-02-04T07:30:48Z)

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.