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Found 11 papers, 4 papers with code
Symmetry Considerations for Learning Task Symmetric Robot Policies
Past methods on encouraging symmetry for robotic tasks have studied this topic mainly in a single-task setting, where symmetry usually refers to symmetry in the motion, such as the gait patterns.
Pedipulate: Enabling Manipulation Skills using a Quadruped Robot's Leg
Additionally, the controller is robust to interaction forces at the foot, disturbances at the base, and slippery contact surfaces.
Self-Supervised Learning of Action Affordances as Interaction Modes
We show in the experiments that such affordance learning predicts interaction which covers most modes of interaction for the querying articulated object and can be fine-tuned to a goal-conditional model.
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim.
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator.
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation
A kitchen assistant needs to operate human-scale objects, such as cabinets and ovens, in unmapped environments with dynamic obstacles.
Neural Lyapunov Model Predictive Control
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides a significant opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account.
Learning Camera Miscalibration Detection
Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications.
Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides an opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account.
Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue
This problem is extremely challenging as pre-existing maps cannot be leveraged for navigation due to structural changes that may have occurred.
Vision-based Autonomous Landing in Catastrophe-Struck Environments
However, these UAVs have to be able to autonomously land on debris piles in order to accurately locate the survivors.
Robotics
