Robot manipulation of multiple objects is an important topic for applications including
warehouse automation, service robots performing cleaning, and large-scale object sorting …

Contact-rich manipulation tasks in unstructured environments often require both haptic and
visual feedback. It is nontrivial to manually design a robot controller that combines these …

Leveraging multimodal information with recursive Bayesian filters improves performance
and robustness of state estimation, as recursive filters can combine different modalities …

Tactile perception is central to robot manipulation in unstructured environments. However, it
requires contact, and a mature implementation must infer object models while also …

We're interested in the problem of estimating object states from touch during manipulation
under occlusions. In this work, we address the problem of estimating object poses from …

A recent line of work has shown that end-to-end optimization of Bayesian filters can be used
to learn state estimators for systems whose underlying models are difficult to hand-design or …

Using sensor data from multiple modalities presents an opportunity to encode redundant
and complementary features that can be useful when one modality is corrupted or noisy …

We address the problem of tracking 3D object poses from touch during in-hand
manipulations. Specifically, we look at tracking small objects using vision-based tactile …

Tracking the pose of an object while it is being held and manipulated by a robot hand is
difficult for vision-based methods due to significant occlusions. Prior works have explored …

We address the problem of learning observation models end-to-end for estimation. Robots
operating in partially observable environments must infer latent states from multiple sensory …