Nikos Aréchiga

Karen Leung, Nikos Aréchiga, Marco Pavone

This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs. STLCG provides a platform which enables the incorporation of logical specifications into robotics problems that benefit from gradient-based solutions. Specifically, STL is a powerful and expressive formal language that can specify spatial and temporal properties of signals generated by both continuous and hybrid systems. The quantitative semantics of STL provide a robustness metric, i.e., how much a signal satisfies or violates an STL specification. In this work, we devise a systematic methodology for translating STL robustness formulas into computation graphs. With this representation, and by leveraging off-the-shelf automatic differentiation tools, we are able to efficiently backpropagate through STL robustness formulas and hence enable a natural and easy-to-use integration of STL specifications with many gradient-based approaches used in robotics. Through a number of examples stemming from various robotics applications, we demonstrate that STLCG is versatile, computationally efficient, and capable of incorporating human-domain knowledge into the problem formulation.

Xin Qin, Nikos Aréchiga, Andrew Best et al.

Autonomous systems such as self-driving cars and general-purpose robots are safety-critical systems that operate in highly uncertain and dynamic environments. We propose an interactive multi-agent framework where the system-under-design is modeled as an ego agent and its environment is modeled by a number of adversarial (ado) agents. For example, a self-driving car is an ego agent whose behavior is influenced by ado agents such as pedestrians, bicyclists, traffic lights, road geometry etc. Given a logical specification of the correct behavior of the ego agent, and a set of constraints that encode reasonable adversarial behavior, our framework reduces the adversarial testing problem to the problem of synthesizing controllers for (constrained) ado agents that cause the ego agent to violate its specifications. Specifically, we explore the use of tabular and deep reinforcement learning approaches for synthesizing adversarial agents. We show that ado agents trained in this fashion are better than traditional falsification or testing techniques because they can generalize to ego agents and environments that differ from the original ego agent. We demonstrate the efficacy of our technique on two real-world case studies from the domain of self-driving cars.