A deep inverse-mapping model for a flapping robotic wing
Authors: Hadar Sharvit, Raz Karl, Tsevi Beatus
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we report a machine-learning solution1 for the inverse mapping of a flapping-wing system based on data from an experimental system we have developed. Our model learns the input wing motion required to generate a desired aerodynamic force outcome. We used a sequence-to-sequence model tailored for time-series data and augmented it with a novel adaptive-spectrum layer that implements representation learning in the frequency domain. To train our model, we developed a flapping wing system that simultaneously measures the wing s aerodynamic force and its 3D motion using high-speed cameras. We demonstrate the performance of our system on an additional open-source dataset of a flapping wing in a different flow regime. Results show superior performance compared with more complex state-of-the-art transformer-based models, with 11% improvement on the test datasets median loss . |
| Researcher Affiliation | Academia | Hadar Sharvit1,2,3, Raz Karl1,2,3 & Tsevi Beatus1,2,3 1 School of Computer Science and Engineering 2 The Institute of Life Sciences 3 Center for Bioengineering The Hebrew University of Jerusalem, Israel 9190401 EMAIL |
| Pseudocode | Yes | Algorithm 1 Adaptive Spectrum Layer (ASL) Require: Tensor x with shape [H, F] (or batched with shape [B, H, F]), Require: fmax, the maximal frequency to consider, and fs the sampling frequency Require: dropout rate p (0, 1) Nf [0, 1, 2, ..., H/2 1, H/2] / (H/fs) ˆx rfft(x) [:, : Nf, :] Real valued Fast Fourier Transform ˆs [|ˆx|, cos ( ˆx), sin ( ˆx)] Stacking phase and magnitude H Re LU(Fully Connected(ˆs)) Hidden representation in Fourier space w Sequential( H dropout[p=p](H) H Fully Connected(H) H H sigmoid(H) H sigmoid(H) ) Gating Mechanism ˆx Padding(ˆx w) with H Nf zeros Results in reconstruction of the same shape x irfft(ˆx) |
| Open Source Code | Yes | 1Framework, models, and data are publicly available on github.com/Hadar933/Adaptive Spectrum Layer |
| Open Datasets | Yes | 1Framework, models, and data are publicly available on github.com/Hadar933/Adaptive Spectrum Layer... The second dataset we used (Table 1, Fig. 4) has been published by Bayiz & Cheng (2021b), who measured the wing kinematics and aerodynamic forces of a plate-like wing flapping in mineral oil... Yagiz Bayiz and Bo Cheng. Flapping wing aerodynamics with prssm [dataset]. https:// datadryad.org/stash/dataset/doi:10.5061/dryad.zgmsbccbs, 2021a. URL https://datadryad.org/stash/dataset/doi:10.5061/dryad.zgmsbccbs. |
| Dataset Splits | Yes | Both datasets were randomly divided such that 75% of the events were used as a training set, 10% reserved for validation, and 15% for testing. |
| Hardware Specification | Yes | training one model took 3min on a single Nvidia RTX 3090 GPU with 24 GB RAM, and an exhaustive hyperparameter search took 50hr on the same hardware. |
| Software Dependencies | No | optimizer_name Choice of Py Torch optimizer. While the paper mentions PyTorch as the optimizer framework, it does not specify a version number for PyTorch or any other software libraries used. Thus, it does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In our framework, we offer the flexibility to explore a larger set of training-related hyperparameters, as specified in Tab. 3 A.3... The parameters explored during this search included: Seq2Seq model args: embedding_size: Varied from 5 to 50 to assess its impact on feature representation. hidden_size: Varied to explore the influence of different hidden dimensions. n_layers: Investigated different numbers of RNN layers (1-3 layers). attn_heads: Explored different numbers of attention heads (1-10). batch size: Explored values ranging from a few dozen to a few thousand. feature window size: Varied (128-512) to assess sensitivity to temporal span. normalizer schemes: every possible pair of normalization schemes for the features and the targets, namely, every pair in {z-score, min-max, none} {z-score, min-max, none}... Concretely, the best-performing parameters are specified in Tab. 4... Table 4: Hyperparameters of the best models. The hyper-parameters used to train the Seq2Seq+ASL model on the validation set of each dataset |