Stanford Robotics Seminar ENGR319 | Spring 2026 | Interactive Autonomy

Real-world robot failures expose reasoning gaps

Recent incidents involving chaotic restaurant robots and gridlocked Waymo vehicles demonstrate current limitations in handling unstructured human environments and multi-agent scenarios.

Theory of mind is essential for safe operation

Safe navigation requires robots to predict how humans and other agents will react to their decisions, similar to how humans mentally model others' behaviors when changing lanes or avoiding hallway collisions.

Nash equilibria model joint decision-making

Formulating interactions as dynamic games where each agent optimizes based on predicted others' actions provides a mathematically elegant framework for joint prediction and planning.

Exact equilibria are computationally prohibitive

Computing Nash equilibria requires solving coupled nonlinear optimal control problems in real-time, which is too expensive for robots operating in receding horizon control loops.

Special structure enables 20x speedup

Real-world robotic interactions often form potential games, allowing equilibrium computation via a single optimal control problem rather than coupled systems, achieving 20x faster solving than traditional game solvers.

Scaling to constrained cooperative tasks

This approach handles high-dimensional systems with explicit constraints, demonstrated by two quadcopters cooperatively transporting a rigid rod while navigating around humans.

Multiple equilibria create ambiguity

Identical scenarios often have multiple valid solutions, such as yielding left versus right, causing collisions when agents select different conventions like the speaker experienced when bumping into people in Singapore.

Real-time adaptation to conventions

Robots can detect which equilibrium humans prefer by observing initial movement directions and adapt in real-time to match local social norms rather than assuming universal conventions.