I will be giving a talk, as part of the IPAB seminar series, through which I will try to further develop this framing of the problem which I expect our group to try to solve in the medium term. In a certain sense, my case will hark back to fairly well established techniques familiar to engineers but slowly lost with the coming of statistical methods into the robotics space. Also, many others are picking up on the underlying needs, e.g., this recent article in the MIT Technology Review gives a popular account of current sentiment among some in the AI community.
The program induction route to explainability and safety in autonomous systems
The confluence of advances in diverse areas including machine learning, large scale computing and reliable commoditised hardware have brought autonomous robots to the point where they are poised to be genuinely a part of our daily lives. Some of the application areas where this seems most imminent, e.g., autonomous vehicles, also bring with them stringent requirements regarding safety, explainability and trustworthiness. These needs seem to be at odds with the ways in which recent successes have been achieved, e.g., with end-to-end learning. In this talk, I will try to make a case for an approach to bridging this gap, through the use of programmatic representations that intermediate between opaque but efficient learning methods and other techniques for reasoning that benefit from ’symbolic’ representations.
I will begin by framing the overall problem, drawing on some of the motivations of the DARPA Explainable AI programme (under the auspices of which we will be starting a new project shortly) and on extant ideas regarding safety and dynamical properties in the control theorists’ toolbox – also noting where new techniques have given rise to new demands.
Then, I will shift focus to results from one specific project, for Grounding and Learning Instances through Demonstration and Eye tracking (GLIDE), which serves as an illustration of the starting point from which we will proceed within the DARPA project. The problem here is to learn the mapping between abstract plan symbols and their physical instances in the environment, i.e., physical symbol grounding, starting from cross-modal input provides the combination of high- level task descriptions (e.g., from a natural language instruction) and a detailed video or joint angles signal. This problem is formulated in terms of a probabilistic generative model and addressed using an algorithm for computationally feasible inference to associate traces of task demonstration to a sequence of fixations which we call fixation programs.
I will conclude with some remarks regarding ongoing work that explicitly addresses the task of learning structured programs, and using them for reasoning about risk analysis, exploration and other forms of introspection.