Search Results for author:
Found 7 papers, 4 papers with code
Integrated Object Deformation and Contact Patch Estimation from Visuo-Tactile Feedback
Representing the object geometry and contact with the environment implicitly allows a single model to predict contact patches of varying complexity.
CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data
Rather than attempting to estimate the true pose of the object, which is not tractable without a large number of contacts, we seek to estimate a plausible set of poses which obey the constraints imposed by the sensor data.
Visuo-Tactile Transformers for Manipulation
Learning representations in the joint domain of vision and touch can improve manipulation dexterity, robustness, and sample-complexity by exploiting mutual information and complementary cues.
TAMPC: A Controller for Escaping Traps in Novel Environments
We propose an approach to online model adaptation and control in the challenging case of hybrid and discontinuous dynamics where actions may lead to difficult-to-escape "trap" states, under a given controller.
Combining Physical Simulators and Object-Based Networks for Control
Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically.
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control.
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Since product images are readily available for a wide range of objects (e. g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data.
