PlasticineLab: A Soft-Body Manipulation Benchmark with Differential Physics

Zhiao Huang    Yuanming Hu    Tao Du

Siyuan Zhou    Hao Su    Joshua B. Tenenbaum    Chuang Gan

Abstract

Alt Text

Simulated virtual environments serve as one of the main driving forces behind developing and evaluating skill learning algorithms. However, existing environments typically only simulate rigid body physics. Additionally, the simulation process usually does not provide gradients that might be useful for planning and control optimizations. We introduce a new differentiable physics benchmark called PasticineLab, which includes a diverse collection of soft body manipulation tasks. In each task, the agent uses manipulators to deform the plasticine into a desired configuration. The underlying physics engine supports differentiable elastic and plastic deformation using the DiffTaichi system, posing many underexplored challenges to robotic agents. We evaluate several existing 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. We expect that PlasticineLab will encourage the development of novel algorithms that combine differentiable physics and model-based RL for more complex physics-based skill learning tasks.

Tasks and Reference Solutions

Alt Text

Quantitative Results

The final normalized incremental IoU score achieved by RL methods within 10^4 epochs. Scores lower than 0 are clamped. The dashed orange line indicates the theoretical upper limit. Alt Text

Rewards and their variances in each task w.r.t. the number of episodes spent on training. We clamp the reward to be greater than 0 for a better illustration. Alt Text

Qualitative Results

Move

Alt Text

TripleMove

Alt Text

Rope

Alt Text

Writer

Alt Text

Chopsticks

Alt Text

Torus

Alt Text

Pinch

Alt Text

Table

Alt Text

Assembly

Alt Text

RollingPin

Alt Text

Paper

PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B. Tenenbaum, Chuang Gan
[Paper][Code][Bibtex]

A moving least squares material point method with displacement discontinuity and two-way rigid body coupling
Yuanming Hu, Yu Fang, Ziheng Ge, Ziyin Qu, Yixin Zhu, Andre Pradhana, and Chenfanfu Jiang
SIGGRAPH 2018

Chainqueen: A real-time differentiable physical simulator for soft robotics
Yuanming Hu, Jiancheng Liu, Andrew Spielberg, Joshua B. Tenenbaum, William T Freeman, Jiajun Wu, Daniela Rus, and Wojciech Matusik
ICRA 2019

Difftaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Fredo Durand
ICLR 2020