6 accepted papers
Robots must adapt to diverse human instructions and operate safely in unstructured, open-world environments. Recent Vision–Language models (VLMs) offer strong priors for grounding language and perception, but remain difficult to steer for navigation due to differences in action spaces and pretrainin…
We address the long-horizon mapless navigation problem: enabling robots to traverse novel environments without relying on high-definition maps or precise waypoints that specify exactly where to navigate. Two major challenges arise: (1) learning robust, generalizable perceptual representations of the…
Anticipating driver intention is an important task when vehicles of mixed and varying levels of human/machine autonomy share roadways. Driver intention can be leveraged to improve road safety, such as warning surrounding vehicles in the event the driver is attempting a dangerous maneuver. In this wo…
Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sop…
Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (Con-vBKI…
This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes. Our data set is formed from randomly sampled views of the world at each time step, which supervises generalizability to complete scenes without occlusions