research
∙
07/11/2023
EgoAdapt: A multi-stream evaluation study of adaptation to real-world egocentric user video
In egocentric action recognition a single population model is typically ...
share
research
∙
03/31/2023
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
We present the largest and most comprehensive empirical study of pre-tra...
research
∙
03/28/2023
BC-IRL: Learning Generalizable Reward Functions from Demonstrations
How well do reward functions learned with inverse reinforcement learning...
research
∙
03/20/2023
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
Object rearrangement is a challenge for embodied agents because solving ...
research
∙
11/04/2022
Neural Grasp Distance Fields for Robot Manipulation
We formulate grasp learning as a neural field and present Neural Grasp D...
research
∙
04/05/2022
Model Based Meta Learning of Critics for Policy Gradients
Being able to seamlessly generalize across different tasks is fundamenta...
research
∙
02/22/2022
Differentiable and Learnable Robot Models
Building differentiable simulations of physical processes has recently r...
research
∙
10/13/2021
Block Contextual MDPs for Continual Learning
In reinforcement learning (RL), when defining a Markov Decision Process ...
research
∙
07/07/2021
Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning
Inverse reinforcement learning is a paradigm motivated by the goal of le...
research
∙
06/28/2021
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
We introduce Habitat 2.0 (H2.0), a simulation platform for training virt...
research
∙
03/14/2021
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication
Effective communication is an important skill for enabling information e...
research
∙
11/24/2020
Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
Navigation policies are commonly learned on idealized cylinder agents in...
research
∙
11/08/2020
Learning Extended Body Schemas from Visual Keypoints for Object Manipulation
Humans have impressive generalization capabilities when it comes to mani...
research
∙
11/07/2020
Leveraging Forward Model Prediction Error for Learning Control
Learning for model based control can be sample-efficient and generalize ...
research
∙
10/29/2020
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
Effective communication is an important skill for enabling information e...
research
∙
10/18/2020
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
Scaling model-based inverse reinforcement learning (IRL) to real robotic...
research
∙
08/27/2020
Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion
Hierarchical learning has been successful at learning generalizable loco...
research
∙
08/18/2020
Residual Learning from Demonstration
Contacts and friction are inherent to nearly all robotic manipulation ta...
research
∙
06/29/2020
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Robots need to be able to adapt to unexpected changes in the environment...
research
∙
03/21/2020
Adversarial Continual Learning
Continual learning aims to learn new tasks without forgetting previously...
research
∙
03/10/2020
Learning State-Dependent Losses for Inverse Dynamics Learning
Being able to quickly adapt to changes in dynamics is paramount in model...
research
∙
01/24/2020
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
The recursive Newton-Euler Algorithm (RNEA) is a popular technique in ro...
research
∙
10/03/2019
Generalized Inner Loop Meta-Learning
Many (but not all) approaches self-qualifying as "meta-learning" in deep...
research
∙
09/26/2019
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Learning to locomote to arbitrary goals on hardware remains a challengin...
research
∙
06/12/2019
Meta-Learning via Learned Loss
We present a meta-learning approach based on learning an adaptive, high-...
research
∙
04/15/2019
Curious iLQR: Resolving Uncertainty in Model-based RL
Curiosity as a means to explore during reinforcement learning problems h...
research
∙
07/11/2018
A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation
One of the challenges in model-based control of stochastic dynamical sys...
research
∙
05/07/2018
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots
Learning for control can acquire controllers for novel robotic tasks, pa...
research
∙
10/24/2017
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
In order to robustly execute a task under environmental uncertainty, a r...
research
∙
10/06/2017
A New Data Source for Inverse Dynamics Learning
Modern robotics is gravitating toward increasingly collaborative human r...
research
∙
10/02/2017
SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control
In this work, we present an approach to deep visuomotor control using st...
research
∙
09/14/2015