Towards a Robotic Intrusion Prevention System: Combining Security and Safety in Cognitive Social Robots

Social Robots need to be safe and reliable to share their space with humans. This paper reports on the first results of a research project that aims to create more safe and reliable, intelligent autonomous robots by investigating the implications and interactions between cybersecurity and safety. We propose creating a robotic intrusion prevention system (RIPS) that follows a novel approach to detect and mitigate intrusions in cognitive social robot systems and other cyber-physical systems. The RIPS detects threats at the robotic communication level and enables mitigation of the cyber-physical threats by using System Modes to define what part of the robotic system reduces or limits its functionality while the system is compromised. We demonstrate the validity of our approach by applying it to a cognitive architecture running in a real social robot that preserves the privacy and safety of humans while facing several cyber attack situations.

Constructing Behavior Trees from Temporal Plans for Robotic Applications

Executing temporal plans in the real and open world requires adapting to uncertainty both in the environment and in the plan actions. A plan executor must therefore be flexible to dispatch actions based on the actual execution conditions. In general, this involves considering both event and time-based constraints between the actions in the plan. A simple temporal network (STN) is a convenient framework for specifying the constraints between actions in the plan. Likewise, a behavior tree (BT) is a convenient framework for controlling the execution flow of the actions in the plan. The principle contributions of this paper are i) an algorithm for transforming a plan into an STN, and ii) an algorithm for transforming an STN into a BT. When combined, these algorithms define a systematic approach for executing total-order (time-triggered) plans in robots operating in the real world. Our approach is based on creating a graph describing a deordered (state-triggered) plan and then creating a BT representing a partial-order (determined at runtime) plan. This approach ensures the correct execution of plans, including those with required concurrency. We demonstrate the validity of our approach within the PlanSys2 framework on real robots.