Self-supervised and Transfer Learning for Adaptive Motion Control

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Problem Motion-control in an unstructured environment is one of the hardest unsolved problems in robotics. Robots are skilled in routine and repetitive tasks, but no robot today can easily handle a novel household task without tedious modelling, designing, and programming by a human. Yet, nature solved this challenge half a billion years ago, and for so many human skills, we are not conscious of how a skill is learned or how a task is achieved. While animals handle complex environments with ease, motion-control is still lagging behind nature. Current state-of-the-art In robotics research, model-based control has shown promising results in manipulators and legged robots. Namely, if we have the perfect information of the kinematics (i.e., position, velocity, shape, dimensionality, etc) and dynamics (i.e., mass, center-of-mass, and inertia tensor, etc) of each rigid body in the system, we can find the control torques needed in order to achieve the desired motion. However, this technique is dependent on the accuracy of the kinematics and the dynamic model. While the data-driven approach has been applied to solve non-trivial tasks, this approach requires lots of human labour and suffers from sensory noises. Objectives The long term objective of my research is to develop robust robot control algorithms that are adaptive and versatile in different scenarios. Over the past decade, the newly developed supervised learning approach has been solving many problems that require finding the mapping between some inputs and outputs. In this proposal, we aim to enhance model-based motion control by the newly developed machine learning techniques. An example scenario that we will be targeting is robot grasping problem. Specifically, our short-term objectives are categorized into the following three themes: Theme 1: Estimation of Kinematics and Dynamics Humans are experts in adapting motion into different scenarios. In order for robots to perform well, the controller needs to have the knowledge of the kinematics and the dynamics model of the objects. For this, the first theme focuses on the estimation of kinematics and dynamics without prior knowledge. Theme 2: Self-supervised Learning Kinesthetic teaching, i.e., to manually move the robots and record the motion, requires a large amount of human labour. This theme focuses on the self-supervised learning approach; namely, let the robot samples different scenarios and creates a supervised learning dataset by itself. The effort will be on designing the criterion that increases the information we can gain from the dataset. Theme 3: Simulation-to-real Transfer Learning This theme takes a simulation-to-real transfer learning approach; namely, the models for kinematics and dynamics will be trained (offline) from data collected in simulation deployed on a real robotic platform. By doing so, we can alleviate the problems arisen from sensory noise and ensures the computation can be done in real-time.