You've Got to Feel It To Believe It: Multi-Modal Bayesian Inference for Semantic and Property Prediction
We incorporate both visual and tactile information to improve semantic maps for robots.
Research Projects
A collection of research projects I have contributed to. My work mainly focuses on probabilistic semantic mapping and active perception.
For a full list of my pbulications, see my Google Scholar.
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
We develop a modular neural network for real-time semantic mapping in uncertain environments.
These Maps Are Made For Walking: Real-Time Terrain Property Estimation for Mobile Robots
This paper proposes a Bayesian inference framework for real-time semantic mapping that uses RGB-D camera data to estimate terrain properties and their probability distributions, outperforming state-of-the-art methods and successfully predicting terrain properties in both simulated and real-world environments.
Generating Continuous Motion and Force Plans in Real-Time for Legged Mobile Manipulation
This paper introduces STORMMAP, a real-time planner for legged manipulators that optimizes center-of-mass motion and manipulation force trajectories to ensure dynamic stability and task completion, running significantly faster than existing methods by assuming pre-determined contact locations.
