Summary of Multimodal Policies with Physics-informed Representations, by Haodong Feng et al.
Multimodal Policies with Physics-informed Representations
by Haodong Feng, Peiyan Hu, Yue Wang, Dixia Fan
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Physics-Informed Representation (PIR) algorithm tackles control problems in partial differential equation (PDE) systems. It combines PINNs with sparse data loss to leverage uncertain observations as states. PIR optimizes neural networks using PDE loss and data loss, allowing it to propagate initial conditions and boundary conditions into inputs. These inputs can be learnable parameters or encoder outputs, serving as the current state representation. The algorithm is tested in downstream control tasks, demonstrating superior consistency with ground truth features, even when modalities are missing. PIR enables robots to navigate complex environments more accurately and efficiently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PDE systems need observations to make decisions, but sensors often have limitations or faults, making them sparse and uncertain. To solve this problem, scientists discovered that neural networks can be used to represent PDE systems using specific losses and data. This led to the creation of a new algorithm called Physics-Informed Representation (PIR). PIR uses two types of loss: one for fitting the neural network and another for calculating data loss on observations with random quantities and modalities. The goal is to represent the current state by propagating initial conditions and boundary conditions into inputs. In experiments, PIR performed better than other algorithms in complex control tasks. |
Keywords
» Artificial intelligence » Encoder » Neural network
