88 citations · 407 across the 41 of their papers we have counts for
12 papers · 1 filter
BC-IRL: Learning Generalizable Reward Functions from Demonstrations
Andrew Szot, Amy Zhang, Dhruv Batra +2
How well do reward functions learned with inverse reinforcement learning (IRL) generalize? We illustrate that state-of-the-art IRL algorithms, which maximize a maximum-entropy obje…
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
Michael Chang, Alyssa L. Dayan, Franziska Meier +3
Object rearrangement is a challenge for embodied agents because solving these tasks requires generalizing across a combinatorially large set of configurations of entities and their…
Cross-Domain Transfer via Semantic Skill Imitation
Karl Pertsch, Ruta Desai, Vikash Kumar +4
We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target do…
Model Based Meta Learning of Critics for Policy Gradients
Sarah Bechtle, Ludovic Righetti, Franziska Meier
Being able to seamlessly generalize across different tasks is fundamental for robots to act in our world. However, learning representations that generalize quickly to new scenarios…
Block Contextual MDPs for Continual Learning
Shagun Sodhani, Franziska Meier, Joelle Pineau +1
In reinforcement learning (RL), when defining a Markov Decision Process (MDP), the environment dynamics is implicitly assumed to be stationary. This assumption of stationarity, whi…
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
Andrew Szot, Alex Clegg, Eric Undersander +18
We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contr…
Adversarial Continual Learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra +2
Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared st…
Generalized Inner Loop Meta-Learning
Edward Grefenstette, Brandon Amos, Denis Yarats +6
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimiza…
Meta-Learning via Learned Loss
Sarah Bechtle, Artem Molchanov, Yevgen Chebotar +4
Typically, loss functions, regularization mechanisms and other important aspects of training parametric models are chosen heuristically from a limited set of options. In this paper…
A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation
Behnoosh Parsa, Keshav Rajasekaran, Franziska Meier +1
One of the challenges in model-based control of stochastic dynamical systems is that the state transition dynamics are involved, and it is not easy or efficient to make good-qualit…
Online Learning of a Memory for Learning Rates
Franziska Meier, Daniel Kappler, Stefan Schaal
The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning ta…
Local Gaussian Regression
Franziska Meier, Philipp Hennig, Stefan Schaal
Locally weighted regression was created as a nonparametric learning method that is computationally efficient, can learn from very large amounts of data and add data incrementally.…