<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository offers a comprehensive survey of embodied learning techniques for object-centric robotic manipulation, addressing the need for intelligent robots capable of interacting with and manipulating objects in complex environments. It targets researchers and engineers in robotics and AI, providing a structured overview of advancements, challenges, and future research directions in this critical domain.

How It Works

The survey categorizes existing research into three main areas: embodied perceptual learning (object pose, affordance prediction), embodied policy learning (reinforcement, imitation learning), and embodied task-oriented learning (grasping, manipulation). It details data representations, learning methods, and task applications, offering a structured landscape of the field.

Quick Start & Requirements

This repository is a curated survey of research papers, not an executable software project. It lists relevant publications, their venues, and links to associated code or project pages where available.

Highlighted Details

• Comprehensive review of embodied learning for object-centric robotic manipulation, covering perceptual, policy, and task-oriented learning.
• Detailed breakdown of data representations (image, 3D, tactile) and policy learning methods (RL, imitation, diffusion).
• Extensive catalog of public datasets, evaluation metrics, applications (industrial, agricultural, domestic, surgical), and identified challenges.
• Includes a link to the survey paper on arXiv (https://arxiv.org/abs/2408.11537) and numerous research publications with code/project pages.

Maintenance & Community

Marked as actively maintained with a "yes" status and welcome for PRs. Last update: August 28, 2024. Contributions encouraged via issues/PRs. No specific community channels listed.

Licensing & Compatibility

Released under the MIT license, which is permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

As a survey, it's a snapshot up to its last update and not a runnable system. The "Challenges and Future Directions" section highlights areas needing further research, such as sim-to-real generalization and multimodal LLMs.