Search Results for author:
Found 5 papers, 1 papers with code
Verifiably Following Complex Robot Instructions with Foundation Models
We propose Language Instruction grounding for Motion Planning (LIMP), a system that leverages foundation models and temporal logics to generate instruction-conditioned semantic maps that enable robots to verifiably follow expressive and long-horizon instructions with open vocabulary referents and complex spatiotemporal constraints.
Exploiting Contextual Structure to Generate Useful Auxiliary Tasks
We propose an approach that maximizes experience reuse while learning to solve a given task by generating and simultaneously learning useful auxiliary tasks.
Sample Efficient Robot Learning with Structured World Models
Reinforcement learning has been demonstrated as a flexible and effective approach for learning a range of continuous control tasks, such as those used by robots to manipulate objects in their environment.
An Exploration of Neural Radiance Field Scene Reconstruction: Synthetic, Real-world and Dynamic Scenes
This project presents an exploration into 3D scene reconstruction of synthetic and real-world scenes using Neural Radiance Field (NeRF) approaches.
Affordable Modular Autonomous Vehicle Development Platform
1. 25 million people die annually from road accidents and Africa has the highest rate of road fatalities [1].
