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Model-Based Reinforcement Learning with LSTM Networks for Non-Prehensile Manipulation Planning
Jeffrey Fong,Domenico Campolo,Cihan Acar,Keng Peng Tee 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Solving non-prehensile manipulation tasks requires domain knowledge involving various interactions such as switching contact dynamics between the robot and the object, and the object-environment interactions. This results in a switched nonlinear dynamic system governing the physical interactions between the object and the environment. In this paper, we propose an interactive learning framework that allows a robot to autonomously learn and model an unknown object’s dynamics, as well as utilise the learned model for efficient planning in completing re-positioning tasks using non-prehensile manipulation. First, we model the overall object dynamics using a Long Short-Term Memory (LSTM) neural network. We then assimilate the learned model into the Monte Carlo Tree Search (MCTS) algorithm with a dense reward function to generate an optimal sequence of push actions for task completion. We demonstrate the framework in both simulated and real robot that pushes objects on a table.
Discrete Task-Space Automatic Curriculum Learning for Robotic Grasping
Anil Kurkcu,Cihan Acar,Domenico Campolo,Keng Peng Tee 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Deep reinforcement learning algorithms struggle in the domain of robotics where data collection is time consuming and in some cases safety-constrained. For sample-efficiency, curriculum learning has shown good results in deep learning-based methods. However, the issue lies on the generation of the curriculum itself, which the field of automatic curriculum learning is trying to solve. We present an automatic curriculum learning algorithm for discrete task-space scenarios. Our curriculum generation is based on difficulty measure between tasks and learning progress metric within a task. We apply our algorithm to a grasp learning problem involving 49 diverse objects. Our results show that a policy trained based on a curriculum is both sample efficient compared to learning from scratch and able to learn tasks that the latter could not learn within a reasonable amount of time.