Manipulation task planning for heterogeneous object stacking based on reinforcement learning
Pham, Minh Khang
Date of Issue2018
School of Mechanical and Aerospace Engineering
The paper propose a new way of solving the Pallet Packing Problem by modelling it as a Markov decision process. This allows the program to make decisions step-by-step based only on the current state and adapt to any error in execution. By applying reinforcement learning techniques, an agent can be trained from simulation to learn a model-free near-optimal policy that maximize the discounted cumulative rewards, which is proportional to the original objective function. Experiments show positive results on simulations involving packing up to 12 boxes into a grid-based pallet of size 8*8*6.
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Final Year Project (FYP)
Nanyang Technological University