UC Berkeley

Industrial robots have been kept upgraded for decades to achieve extraordinary accuracy, speed, and repeatability. However, even the most advanced manipulator today is functioning as a programmable machine, instead of an intelligent agent. This deficiency of intelligence restricts robots from broader applications. To meet the increasing demand for automation, it is essential to make industrial robots more skillful and intelligent. Under this background, the objective of this dissertation is to develop generic and efficient methodologies to teach robots novel skills. Three major approaches including model-based learning, model-free learning and analogy-based learning are discussed and explored. A series of skills such as assembly, grasping, tracking and motion planning, have been successfully taught to industrial manipulators and evaluated by experiments.

As the name implies, model-based learning tries to formulate skills analytically based on physical models. In Chapter 2, an auto-alignment skill is developed for robotic assembly by constructing a novel contact model. Robots are enabled to predict tilt angles between assembly parts from force/torque measurement and perform fine assembly from large misalignment conditions. With this skill model, traditional procedures such as the installation of positioning fixtures and manual alignment can be skipped, which saves tremendous preparation efforts for robotic assembly.

However, not always system models can be constructed, especially for those complicated scenarios. The model-free approach is then developed to learn control policies by regressing general parametric functions. In Chapter 3, a compliant robotic force controller is learned from human demonstration. A human operator holds a specially-designed handle and demonstrates to robots the compliant insertions. The Gaussian mixture regression is introduced to fit motion patterns from measured data. This approach enables to transfer the compliant assembly skill from humans to robots efficiently and intuitively.

Besides model-based and model-free learning, an analogy-based leaning approach is proposed in Chapter 4. The distinct idea is that instead of pursuing a control policy either constructed by models or regressed from data, we discover the correlation between scenarios, i.e., analogy. The past scenario that bears a strong similarity with the current one will be identified, and a mapping function between these two scenes will be constructed. Applying the mapping function, the past problem-solving action can be transferred to a new one that is feasible for the current scenario. This analogy-based approach has been implemented in multiple industrial tasks, and taught robots various skills such as grasping versatile objects (Chapter 5), tracking and manipulating deformable objects (Chapter 6) and efficient motion re-planning for similar scenarios (Chapter 7).