Counterexample-Guided Synthesis of Perception Models and Control

Recent advances in learning-based perception systems have led to drastic improvements in the performance of robotic systems like autonomous vehicles and surgical robots. These perception systems, however, are hard to analyze and errors in them can propagate to cause catastrophic failures. In this paper, we consider the problem of synthesizing safe and robust controllers for robotic systems which rely on complex perception modules for feedback. We propose a counterexample-guided synthesis framework that iteratively builds simple surrogate models of the complex perception module and enables us to find safe control policies. The framework uses a falsifier to find counterexamples, or traces of the systems that violate a safety property, to extract information that enables efficient modeling of the perception modules and errors in it. These models are then used to synthesize controllers that are robust to errors in perception. If the resulting policy is not safe, we gather new counterexamples. By repeating the process, we eventually find a controller which can keep the system safe even when there is a perception failure. We demonstrate our framework on two scenarios in simulation, namely lane keeping and automatic braking, and show that it generates controllers that are safe, as well as a simpler model of a deep neural network-based perception system that can provide meaningful insight into operations of the perception system

Co-design of Control and Planning for Multi-rotor UAVs with Signal Temporal Logic Specifications

Urban Air Mobility (UAM), or the scenario where multiple manned and Unmanned Aerial Vehicles (UAVs) carry out various tasks over urban airspaces, is a transportation concept of the future that is gaining prominence. UAM missions with complex spatial, temporal and reactive requirements can be succinctly represented using Signal Temporal Logic (STL), a behavioral specification language. However, planning and control of systems with STL specifications is computationally intensive, usually resulting in planning approaches that do not guarantee dynamical feasibility, or control approaches that cannot handle complex STL specifications. Here, we present an approach to co-design the planner and control such that a given STL specification (possibly over multiple UAV s) is satisfied with trajectories that are dynamically feasible and our controller can track them with a bounded tracking-error that the planner accounts for. The tracking controller is formulated for the non-linear dynamics of the individual UAVs, and the tracking error bound is computed for this controller when the trajectories satisfy some kinematic constraints. We also augment an existing multi-UAV STL-based trajectory generator in order to generate trajectories that satisfy such constraints. We show that this co-design allows for trajectories that satisfy a given STL specification, and are also dynamically feasible in the sense that they can be tracked with bounded error. The applicability of this approach is demonstrated through simulations of multi- UAV missions

Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World

We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world. Our approach is based on formal methods, combining formal specification of scenarios and safety properties, algorithmic test case generation using formal simulation, test case selection for track testing, executing test cases on the track, and analyzing the resulting data. Experiments with a real autonomous vehicle at an industrial testing facility support our hypotheses that (i) formal simulation can be effective at identifying test cases to run on the track, and (ii) the gap between simulated and real worlds can be systematically evaluated and bridged

Functional analysis and consequences of Mdm2 E3 ligase inhibition in human tumor cells

Mdm2 is the major negative regulator of p53 tumor suppressor activity. This oncoprotein is overexpressed in many human tumors that retain the wild type p53 allele. As such, targeted inhibition of Mdm2 is being considered as a therapeutic anticancer strategy. The N-terminal hydrophobic pocket of Mdm2 binds to p53 and thereby inhibits the transcription of p53 target genes. Additionally, the C-terminus of Mdm2 contains a RING domain with intrinsic ubiquitin E3 ligase activity. By recruiting E2 ubiquitin conjugating enzyme(s), Mdm2 acts as a molecular scaffold to facilitate p53 ubiquitination and proteasome-dependent degradation. Mdmx (Mdm4), an Mdm2 homolog, also has a RING domain and hetero-oligomerizes with Mdm2 to stimulate its E3 ligase activity. Recent studies have shown that C-terminal residues adjacent to the RING domain of both Mdm2 and Mdmx contribute to Mdm2 E3 ligase activity. However, the molecular mechanisms mediating this process remain unclear, and the biological consequences of inhibiting Mdm2/Mdmx co-operation or blocking Mdm2 ligase function are relatively unexplored. This study presents biochemical and cell biological data that further elucidate the mechanisms by which Mdm2 and Mdmx co-operate to regulate p53 level and activity. We use chemical and genetic approaches to demonstrate that functional inhibition of Mdm2 ubiquitin ligase activity is insufficient for p53 activation. This unexpected result suggests that concomitant treatment with Mdm2/Mdmx antagonists may be needed to achieve therapeutic benefit