CHAPTER 6: CONCLUSION AND FUTURE WORK
6.2 Future Work
We identify three avenues of future work. In future work, we would like to provide stronger guarantees, including probabilistic completeness and asymptotic optimality. One method to provide these guarantees is to use sampling-based methods such as RRT*, which can be combined with our nonlinear dimension reduction techniques proposed in Chapter4 to reduce the sampling complexity. Another technique to provide completeness guarantee is global optimization such as mixed-integer optimization (Ding et al.2011) and particle swarm optimization (Masehian and Sedighizadeh2013). Second, we would like to study learning-based methods to achieve realtime feedback motion planning. In Chapter2, we developed a learning-based feedback motion planner for liquid transfer problems using supervised learning. However, the generality of the learned controller is limited and the
robustness of the controller in terms of environmental uncertainty has not been systematically evaluated. In future work, we would like to use our optimization-based motion planner in model- based RL to create more robust motion planners. In our recent work (P. Ma et al.2018), we have experimented model-free RL in the control of coupled fluid-rigid systems. However, model-free RL requires too many samples making training for practical scenarios prohibitively costly. Using model-based RL can potentially reduce the number of required samples. Finally, we would like to transfer our learned motion plans to physical robot hardware. This will require us to tackle the model discrepancy between real and simulated environments and the uncertainty in sensing and control. In our work (Jia, Z. Pan, et al.2019), we have transferred a particular controller for the manipulation of cloth to the ABB YuMi robot. Future research will focus on a more systematic method to transfer general controllers to physical robot systems.
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