PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
Authors: Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B. Tenenbaum, Chuang Gan
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark. Experimental results suggest that 1) RL-based approaches struggle to solve most of the tasks efficiently; 2) gradient-based approaches, by optimizing open-loop control sequences with the built-in differentiable physics engine, can rapidly find a solution within tens of iterations, but still fall short on multi-stage tasks that require long-term planning. |
| Researcher Affiliation | Collaboration | Zhiao Huang UC San Diego Yuanming Hu MIT Tao Du MIT Siyuan Zhou Peking University Hao Su UC San Diego Joshua B. Tenenbaum MIT BCS, CBMM, CSAIL Chuang Gan MIT-IBM Watson AI Lab |
| Pseudocode | Yes | def von_Mises_return_mapping(F): # F is the deformation gradient before return mapping U, sig, V = ti.svd(F) epsilon = ti.Vector([ti.log(sig[0, 0]), ti.log(sig[1, 1])]) epsilon_hat = epsilon (epsilon.sum() / 2) epsilon_hat_norm = epsilon_hat.norm() delta_gamma = epsilon_hat_norm yield_stress / (2 * mu) if delta_gamma > 0: # Yields! epsilon -= (delta_gamma / epsilon_hat_norm) * epsilon_hat sig = make_matrix_from_diag(ti.exp(epsilon)) F = U @ sig @ V.transpose() return F |
| Open Source Code | Yes | Plasticine Lab is publicly available 1. 1Project page: http://plasticinelab.csail.mit.edu |
| Open Datasets | No | The paper introduces a new benchmark/environment called Plasticine Lab, which itself serves as the data for evaluation rather than using an existing public dataset for training. The environment provides tasks and configurations. |
| Dataset Splits | No | The paper discusses an "evaluation suite" and generating "configurations" for each task. It evaluates performance on these configurations and compares different methods, which serves a similar purpose to testing. However, it does not explicitly define distinct "validation splits" or a specific validation methodology for model tuning as part of its experimental setup. |
| Hardware Specification | Yes | Table 2: Performance on an NVIDIA GTX 1080 Ti GPU. We show the average running time for a single forward or forward + backpropagation step for each scene. |
| Software Dependencies | No | The paper mentions key software components such as Taichi, Diff Taichi, MLS-MPM, and CUDA. However, it does not specify concrete version numbers for any of these software dependencies, which would be necessary for full reproducibility. |
| Experiment Setup | Yes | We train each algorithm on each configuration for 10000 episodes, with 50 environment steps per episode. We list part of the hyperparameters in Table 3 for SAC, Table 5 for TD3 and Table 4 for PPO. We fix c1 = 10, c2 = 10 and c3 = 1 for all environments reward. |