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Found 6 papers, 2 papers with code
Learning Robot Manipulation from Cross-Morphology Demonstration
Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student.
Learning Deformable Object Manipulation from Expert Demonstrations
We test DMfD on a set of representative manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the SoftGym suite of tasks, each with state and image observations.
Deformable Object Manipulation
General Reinforcement Learning
+1
Adaptive Sampling using POMDPs with Domain-Specific Considerations
We find that by using knowledge of the number of rollouts allocated, the agent can more effectively choose actions to explore.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution.
Robotics
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.
