Robotic technology has transformed manufacturing industry ever since the first industrial
robot was put in use in the beginning of the 60s. The challenge of develo** flexible …

We present FoundationPose a unified foundation model for 6D object pose estimation and
tracking supporting both model-based and model-free setups. Our approach can be instantly …

We present a near real-time (10Hz) method for 6-DoF tracking of an unknown object from a
monocular RGBD video sequence, while simultaneously performing neural 3D …

Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …

We present a novel approach to perform object-independent grasp synthesis from depth
images via deep neural networks. Our generative gras** convolutional neural network …

Sampling-based model-predictive control (MPC) is a promising tool for feedback control of
robots with complex, non-smooth dynamics, and cost functions. However, the …

Fast feedback control and safety guarantees are essential in modern robotics. We present
an approach that achieves both by combining novel robust model predictive control (MPC) …

Tracking the 6D pose of objects in video sequences is important for robot manipulation. Most
prior efforts, however, often assume that the target object's CAD model, at least at a category …

Large Language Models (LLMs) have been shown to be capable of performing high-level
planning for long-horizon robotics tasks, yet existing methods require access to a pre …

Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive
results in recent years. While many studies in RL focus on varying the observation space or …