Events

Robot Autonomy with Feedback Systems of Search and Deep Learning

Speaker: Benjamin Riviere, California Institute of Technology

Location: TBA

Date: Wednesday, March 19, 2025

Location: 5MTC, LC400

From self-driving cars to space exploration, autonomous robots are poised to transform industry and science. A promising strategy for autonomous decision-making is to combine search-based predictive reasoning and data-driven learning, which has empirically demonstrated superior performance and interpretability, e.g., the Go-playing AlphaZero algorithm. However, applying this combination to robotics is challenging because robots navigate a continuous, unstructured and high-dimensional physical world that is difficult to traverse efficiently with search trees in real-time. In this talk, I present two main results towards feedback systems of search and learning for autonomy in complex physical systems: First, to enable tree search on continuous systems, I use the spectral decomposition of the local controllability Grammian to construct trees that capture rich dynamical information like contact modes and actuator degradation. Second, to characterize the robot’s real-time thinking process, I present our optimality convergence result of Monte Carlo Tree Search. Our solutions enable new capabilities in complex hardware experiments spanning active space debris removal, fault diagnosis isolation and recovery, swarm vs swarm drone games, wind-aware navigation, planning for self-driving cars (simulated) and dexterous manipulation (ongoing). This work builds towards a sophisticated model of robot autonomy that blends intelligence modalities and real-time and offline computation.