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Found 9 papers, 2 papers with code
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
In this work, we propose a method for precise relative pose prediction which is provably SE(3)-equivariant, can be learned from only a few demonstrations, and can generalize across variations in a class of objects.
On Time-Indexing as Inductive Bias in Deep RL for Sequential Manipulation Tasks
In this paper we explore a simple structure which is conducive to skill learning required for so many of the manipulation tasks.
FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects
We propose a vision-based system that learns to predict the potential motions of the parts of a variety of articulated objects to guide downstream motion planning of the system to articulate the objects.
Self-supervised Transparent Liquid Segmentation for Robotic Pouring
Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem.
Robotic Grasping through Combined image-Based Grasp Proposal and 3D Reconstruction
We present a novel approach to robotic grasp planning using both a learned grasp proposal network and a learned 3D shape reconstruction network.
Robotics
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error
We implement the objective with an adversarial Q-learning method in which Q and Qx are the action-value functions for extrinsic and secondary rewards, respectively.
Reward Prediction Error as an Exploration Objective in Deep RL
We then propose a deep reinforcement learning method, QXplore, which exploits the temporal difference error of a Q-function to solve hard exploration tasks in high-dimensional MDPs.
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies
CGP aims to combine the stability and performance of iterative sampling policies with the low computational cost of a policy network.
emoji2vec: Learning Emoji Representations from their Description
Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings.
