Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
Authors: Sizhe Li, Zhiao Huang, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results suggest that: 1) on multi-stage tasks that are infeasible for the vanilla differentiable physics solver, our approach discovers contact points that efficiently guide the solver to completion; 2) on tasks where the vanilla solver performs sub-optimally or near-optimally, our contact point discovery method performs better than or on par with the manipulation performance obtained with handcrafted contact points. |
| Researcher Affiliation | Collaboration | Sizhe Li University of Rochester sli96@u.rochester.edu; Zhiao Huang UC San Diego z2huang@eng.ucsd.edu; Tao Du MIT taodu@csail.mit.edu; Hao Su UC San Diego haosu@eng.ucsd.edu; Joshua B. Tenenbaum MIT BCS, CBMM, CSAIL jbt@mit.edu; Chuang Gan MIT-IBM Watson AI Lab ganchuang@csail.mit.edu |
| Pseudocode | Yes | C PSEUDOCODE OF CPDEFORM; Algorithm 1 CPDeform; D CONTROLLER OPTIMIZATION WITH DIFFERENTIABLE PHYSICS; Algorithm 2 Differentiable Physics Solver for Controller Optimization |
| Open Source Code | No | Demos are available on our project page1. 1Project Page: http://cpdeform.csail.mit.edu. This is a project page and does not explicitly state that the source code for the methodology is provided there, nor is it a specific code repository. |
| Open Datasets | Yes | To extensively evaluate our method, we introduce Plasticine Lab-M, a dataset that extends the existing differentiable physics benchmark Plasticine Lab with seven new challenging multi-stage soft-body manipulation tasks, and contains the multi-stage environment Pinch in Plasticine Lab. |
| Dataset Splits | No | The paper does not provide explicit details about training, validation, and test dataset splits (e.g., percentages, sample counts, or specific files). It mentions episodes and steps for optimization/training but not data partitioning. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, or cloud resources with specs) used to run its experiments. |
| Software Dependencies | No | The paper mentions software like 'differentiable physics solver' and 'SAC', 'PPO', 'TD3' but does not provide specific version numbers for any software components or libraries. |
| Experiment Setup | Yes | For each stage, we optimize for 200 episodes for differentiable physics-based approaches with a learning rate 0.1. For each environment, we modestly choose a horizon of 10 or 20. We restrict the number of environment steps used for optimization under 1 million. We train each RL algorithm on each environment for 1000 episodes, with 1000 environment steps per episode, which accounts for the 1 million environment-step limit. |