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Found 10 papers, 2 papers with code
Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning
We show that representing objects as signed-distance fields not only enables to learn and represent a variety of models with higher accuracy compared to point-cloud and occupancy measure representations, but also that SDF-based models are suitable for optimization-based planning.
Learning Neural Implicit Functions as Object Representations for Robotic Manipulation
Robotic manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e. g., grasp, placement, tool-use, etc.
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly.
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image
This is possible by encoding the objects of the scene in images as input to the neural network, instead of a fixed feature vector.
Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning
Physical reasoning is a core aspect of intelligence in animals and humans.
Robotics
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly.
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional sequential raw data, e. g., video.
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional \textit{sequential} raw data, e. g., video.
Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models
Second, an approximate inference algorithm is used, exploiting through the duality between control and estimation, to explore the decision space and to compute a high-quality motion trajectory of the robot.
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets
This work presents a multiscale framework to solve an inverse reinforcement learning (IRL) problem for continuous-time/state stochastic systems.
