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Found 11 papers, 3 papers with code
DEF-oriCORN: efficient 3D scene understanding for robust language-directed manipulation without demonstrations
We present DEF-oriCORN, a framework for language-directed manipulation tasks.
Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learning
Although reinforcement learning technique has fostered the advancement of socially aware navigation, defining appropriate reward functions, especially in congested environments, has posed a significant challenge.
Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators
Their main issue lies in contact detection (CD): existing CD algorithms, such as Gilbert-Johnson-Keerthi (GJK), must trade off their computational speed with accuracy which becomes expensive as the number of collisions among non-convex objects increases.
Representation, learning, and planning algorithms for geometric task and motion planning
The first is an algorithm for learning a rank function that guides the discrete task level search, and the second is an algorithm for learning a sampler that guides the continuous motionlevel search.
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i. e. without prior object models.
Integrated Task and Motion Planning
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the objects, is known as task and motion planning (TAMP).
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent.
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Bayesian optimization usually assumes that a Bayesian prior is given.
Learning to guide task and motion planning using score-space representation
In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems.
Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
For such complex planning problems, unguided uniform sampling of actions until a path to a goal is found is hopelessly inefficient, and gradient-based approaches often fall short when the optimization manifold of a given problem is not smooth.
Learning from Limited Demonstrations
We achieve this by integrating LfD in an approximate policy iteration algorithm.
