96 citations · 456 across the 88 of their papers we have counts for
7 papers · 1 filter
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
Jonathan Chang, Nishanth Kumar, Sean Hastings +6
Robots need to learn skills that can not only generalize across similar problems but also be directed to a specific goal. Previous methods either train a new skill for every differ…
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Oliver Kroemer, Scott Niekum, George Konidaris
A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen subs…
Probabilistic Category-Level Pose Estimation via Segmentation and Predicted-Shape Priors
Benjamin Burchfiel, George Konidaris
We introduce a new method for category-level pose estimation which produces a distribution over predicted poses by integrating 3D shape estimates from a generative object model wit…
Learning Portable Representations for High-Level Planning
Steven James, Benjamin Rosman, George Konidaris
We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representat…
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces
Craig J. Bester, Steven D. James, George D. Konidaris
Parameterised actions in reinforcement learning are composed of discrete actions with continuous action-parameters. This provides a framework for solving complex domains that requi…
Grounding Language Attributes to Objects using Bayesian Eigenobjects
Vanya Cohen, Benjamin Burchfiel, Thao Nguyen +3
We develop a system to disambiguate object instances within the same class based on simple physical descriptions. The system takes as input a natural language phrase and a depth im…
Discovering Options for Exploration by Minimizing Cover Time
Yuu Jinnai, Jee Won Park, David Abel +1
One of the main challenges in reinforcement learning is solving tasks with sparse reward. We show that the difficulty of discovering a distant rewarding state in an MDP is bounded…