Task-Agnostic Morphology Evolution
Authors: Donald Joseph Hejna III, Pieter Abbeel, Lerrel Pinto
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate that across 2D, 3D, and manipulation environments TAME can evolve morphologies that match the multi-task performance of those learned with task supervised algorithms. |
| Researcher Affiliation | Academia | Donald J. Hejna III UC Berkeley jhejna@berkeley.edu Pieter Abbeel UC Berkeley pabbeel@berkeley.edu Lerrel Pinto New York University lerrel@cs.nyu.edu |
| Pseudocode | Yes | Algorithm 1: TAME Init. qφ(aj|s T , m) and population P; for i = 1, 2, ..., N do for j = 1, 2..., L do m mutation from P; for k = 1, 2, ..., E do sample joint actions a; s T end State(a, m); P P {(m, )}; φ train(qφ, P); for (m, f) in P do f update via equation 1; return arg max(m,f) P f |
| Open Source Code | Yes | Our code and videos can be found at https://sites.google.com/view/task-agnostic-evolution. |
| Open Datasets | No | Note that since there are no standard multi-task morphology optimization benchmarks, we created our own morphology representation and set of multi-task environments that will be publicly released. The paper does not specify a pre-existing public dataset for training, but rather generates data within custom environments that are stated to be publicly released with their code. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits, as it generates data dynamically through simulation for policy training and evaluation rather than using a static pre-split dataset. |
| Hardware Specification | No | The paper states 'We thank AWS for computing resources.' and mentions 'TAME running on two CPU cores' and 'NGE-Like algorithm running on eight CPU cores' in Table 2, but does not provide specific hardware models like CPU or GPU types. |
| Software Dependencies | No | The paper mentions 'Pytorch Geometric software package' and 'Stable-Baselines 3' but does not specify their version numbers. |
| Experiment Setup | Yes | Further experiment details can be found in Appendix G. Table 6 lists PPO Hyperparameters (e.g., 'Discount 0.99', 'batch size 128', 'learning rate 0.0003') and Table 7 lists Evolution hyperparameters (e.g., 'generations 60', 'population size 24', 'learning rate 0.001'). |