Action Generative Networks Planning for Deformable Object with Raw Observations
Abstract
:1. Introduction
2. Related Work
2.1. Deformable Object Planning
2.2. Contrastive Prediction
3. Our AGN Approach
3.1. Problem Definition
3.2. Algorithm Framework
3.3. Planning with AGN
 Firstly, stateabstractor model E outputs abstract state ${s}_{0}$ and ${s}_{g}$ with ${o}_{0}$ and ${o}_{g}$, respectively.
 Secondly, we compute an action sequence reaching ${s}_{g}$ from ${s}_{0}$ and derive an action state trajectory $\gamma =\langle {s}_{0},{a}_{0},{s}_{1},{a}_{1},\dots ,{a}_{N1},{s}_{N}\rangle $ by Algorithm 1. We first perform linear interpolation between ${s}_{0}$ and ${s}_{g}$, and attain an initial sequence $\eta $ = $[{s}_{0},{s}_{1},\cdots ,{s}_{n},{s}_{g}]$. As for each pair of ${s}_{i}$ and ${s}_{i+1}$, we compute an action ${a}_{i}$ by the heuristic model. If ${s}_{i}$ can reach ${s}_{i+1}$ after executing action ${a}_{i}$, we add state ${s}_{i}$ and action ${a}_{i}$ into $\theta $. Otherwise, we interpolate a latent state ${s}_{mid}$ into $\eta $ between ${s}_{i}$ and ${s}_{i+1}$. We repeat the above procedures until each pair of states in $\eta $ can be transformed by an action computed by the heuristic model. Finally, we attain an action state trajectory $\gamma =\langle {s}_{0},{a}_{0},{s}_{1},{a}_{1},\dots ,{a}_{N1},{s}_{N}\rangle $.
Algorithm 1 planning algorithm. 
input:${s}_{0}$, ${s}_{g}$, F, T. 
output:$\gamma =\langle {s}_{0},{a}_{0},{s}_{1},{a}_{1},\dots ,{a}_{N1},{s}_{N}\rangle $ 

 Finally, we compute an action observation trajectory $\sigma =\langle {o}_{0},{a}_{0},{o}_{1},{a}_{1},\dots ,$${a}_{N1},{o}_{N}\rangle $. We first sample k different Gaussian noises randomly. Then we can obtain k different action observation trajectories given an action state trajectory $\theta $ and a noise by stategenerator model D. At last, we select an optimal action observation trajectory $\sigma =\langle {o}_{0},{a}_{0},{o}_{1},{a}_{1},\dots ,{a}_{N1},{o}_{N}\rangle $ among the k trajectories.
4. Experiments
4.1. Baselines
4.2. Evaluation Criterion
 Trajectory confidence, to evaluate whether an observation transition is feasible or not.
 Trajectory distance, to evaluate the Euclidean distance between the current observation and the next observation after the current action is performed.
 Finaltogoal distance, to evaluate the Euclidean distance between the final observation and goal observation.
 The Judge model takes a pair of observations $({o}_{t},{o}_{t+1})$ as input and outputs a binary result of whether the observation is feasible or not. The training dataset consists of positive observation pairs, which are 1 timestep apart, and negative pairs that are randomly sampled from different rope manipulation trajectories. To avoid the background of rope influencing the training of Judge, we preprocess the rope data using the background subtraction pipeline mentioned above.To validate the accuracy of the Judge model, we evaluate it with observation traces to observe the binary outputs. Given an mlength observation trace, Judge takes the first observation and an observation, which is n steps apart, where n is from 1 to $m1$. The binary output decreases from 1 to 0 smoothly with n increasing, indicating that the Judge model has the ability to recognize a feasible observation pair. We test Judge with 100 traces out of the testing dataset for AGN and the accuracy is 98%.
 The EVAL model takes a pair of observations $({o}_{t},{\widehat{o}}_{t+1})$, an action ${a}_{t}$, and an observation ${o}_{t+1}$ as inputs, where ${\widehat{o}}_{t+1}$ is a predicting next observation and ${o}_{i+1}$ is a real next observation, they are updated from a current observation ${o}_{t}$ after executing action ${a}_{t}$. The EVAL model outputs a distance between ${o}_{t+1}$ and ${\widehat{o}}_{t+1}$. The training dataset consists of positive next observations, we trained the EVAL model by letting the predict next observation ${\widehat{o}}_{t+1}$ be close to the real next observation ${o}_{t+1}$. On a heldout test set, the distance between the predict next observation and real next observation converges to 0.
4.3. Rope Manipulation
4.4. Cloth Manipulation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Trajectory Confidence  Trajectory Distance  Final–to–Goal Distance  

visualforward  0.719  9.7189  5.5484 
jointdynamics  0.567  10.680  5.046 
ausalinfoGAN  0.884  9.0219  2.29 
AGN  0.935  1.432  2.126 
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Sheng, Z.; Jin, K.; Ma, Z.; Zhuo, H.H. Action Generative Networks Planning for Deformable Object with Raw Observations. Sensors 2021, 21, 4552. https://doi.org/10.3390/s21134552
Sheng Z, Jin K, Ma Z, Zhuo HH. Action Generative Networks Planning for Deformable Object with Raw Observations. Sensors. 2021; 21(13):4552. https://doi.org/10.3390/s21134552
Chicago/Turabian StyleSheng, Ziqi, Kebing Jin, Zhihao Ma, and HankzHankui Zhuo. 2021. "Action Generative Networks Planning for Deformable Object with Raw Observations" Sensors 21, no. 13: 4552. https://doi.org/10.3390/s21134552