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Found 9 papers, 5 papers with code
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations.
Pathfinder Discovery Networks for Neural Message Passing
Additional results from a challenging suite of node classification experiments show how PDNs can learn a wider class of functions than existing baselines.
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment.
Learning Equality Constraints for Motion Planning on Manifolds
Constrained robot motion planning is a widely used technique to solve complex robot tasks.
Learning Manifolds for Sequential Motion Planning
Motion planning with constraints is an important part of many real-world robotic systems.
Robotics Computational Geometry
Sampling-Based Motion Planning on Sequenced Manifolds
We address the problem of planning robot motions in constrained configuration spaces where the constraints change throughout the motion.
Robotics Computational Geometry
Kinematic Morphing Networks for Manipulation Skill Transfer
The transfer of a robot skill between different geometric environments is non-trivial since a wide variety of environments exists, sensor observations as well as robot motions are high-dimensional, and the environment might only be partially observed.
Identification of Unmodeled Objects from Symbolic Descriptions
Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand.
Multi-Task Policy Search
Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics.
