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[1908.00722] Learning to combine primitive skills: A step towards versatile robotic manipulation
Source: ar5iv.labs.arxiv.org
Article summary:
1. This article proposes a reinforcement learning (RL) approach to task planning that learns to combine primitive skills, which requires neither intermediate rewards nor complete task demonstrations during training.
2. The proposed vision-based task planning is demonstrated in challenging settings with temporary occlusions and dynamic scene changes.
3. An efficient training of basic skills from few synthetic demonstrations is proposed by exploring recent CNN architectures and data augmentation.
Article analysis:
This article presents a novel approach for robotic manipulation tasks using reinforcement learning (RL). The authors propose an RL-based method that learns to combine simple imitation-based policies, which simplifies RL by reducing its exploration to sequences with a limited number of primitive actions, or “skills”. The authors demonstrate the versatility of their vision-based task planning in challenging settings with temporary occlusions and dynamic scene changes, as well as an efficient training of basic skills from few synthetic demonstrations by exploring recent CNN architectures and data augmentation.
The trustworthiness and reliability of this article can be assessed based on several criteria such as the evidence presented for the claims made, the potential biases and their sources, one-sided reporting, missing points of consideration, missing evidence for the claims made, unexplored counterarguments, promotional content, partiality, whether possible risks are noted, not presenting both sides equally etc. In this regard, the article appears to be reliable and trustworthy since it provides sufficient evidence for its claims through experiments conducted on real UR5 robotic arms as well as simulations in simulated environments. Furthermore, it does not appear to have any potential biases or one-sided reporting since it presents both sides equally and does not promote any particular point of view or agenda. Additionally, all possible risks associated with the proposed approach are noted in the article. Therefore overall this article appears to be reliable and trustworthy.