SayNav is a novel planning framework, that leverages human knowledge from Large Language Models (LLMs) to dynamically generate step-by-step instructions for autonomous agents to complicated navigation tasks in unknown large-scale environments. It also enables efficient generalization of learning to navigate from simulation to real novel environments.
SRI’s vision-based systems enable safe operations of moving unmanned ground vehicles around stationary and moving people in urban/cluttered environments. Under the Navy Explosive Ordnance Disposal project, SRI has developed a real-time, fused-sensor system that significantly improves stationary and dynamic object detection, pedestrian classification, and tracking capabilities from a moving unmanned ground vehicle (UGV). The system…
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments SRI International has developed a new learning-based approach to enable the mobile robot to resemble human capabilities in semantic understanding. The robot can employ semantic scene structures to reason about the world and pay particular attention to relevant semantic landmarks to develop navigation strategies.…