Summary of Learning Manipulation Tasks in Dynamic and Shared 3d Spaces, by Hariharan Arunachalam et al.
Learning Manipulation Tasks in Dynamic and Shared 3D Spaces
by Hariharan Arunachalam, Marc Hanheide, Sariah Mghames
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep reinforcement learning strategy learns multi-categorical item placement in a shared workspace between dual-manipulators and multi-goal destinations, assuming pick-and-place operations are already completed. The approach leverages a stochastic actor-critic framework to train an agent’s policy network and a dynamic 3D Gym environment with static and dynamic obstacles, such as human factors and robot mates. Experiments in a Gazebo simulator show an increase in cumulative reward function for the agent when it is further away from human factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to teach robots how to sort items into different categories using deep learning techniques. Robots already know how to pick up objects, but now they need to learn how to put them down in the right place. The researchers created a special kind of computer simulation that mimics real-world scenarios and tested their approach with good results. |
Keywords
» Artificial intelligence » Deep learning » Reinforcement learning
