Search Results for author:
Found 12 papers, 2 papers with code
Adaptive Mobile Manipulation for Articulated Objects In the Open World
We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20, 000 USD.
Efficient RL via Disentangled Environment and Agent Representations
Agents that are aware of the separation between themselves and their environments can leverage this understanding to form effective representations of visual input.
Structured World Models from Human Videos
We propose an approach for robots to efficiently learn manipulation skills using only a handful of real-world interaction trajectories from many different settings.
Affordances from Human Videos as a Versatile Representation for Robotics
Utilizing internet videos of human behavior, we train a visual affordance model that estimates where and how in the scene a human is likely to interact.
ALAN: Autonomously Exploring Robotic Agents in the Real World
Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision.
Discovering and Achieving Goals via World Models
How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision?
Discovering and Achieving Goals with World Models
How can an artificial agent learn to solve a wide range of tasks in a complex visual environment in the absence of external supervision?
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling
Our method is based on a simple insight: we recognize that dynamics models can be adapted efficiently and consistently with off-policy data, more easily than policies and value functions.
Consistent Meta-Reinforcement Learning via Model Identification and Experience Relabeling
Reinforcement learning algorithms can acquire policies for complex tasks automatically, however the number of samples required to learn a diverse set of skills can be prohibitively large.
Learning to Reinforcement Learn by Imitation
Meta-reinforcement learning aims to learn fast reinforcement learning (RL) procedures that can be applied to new tasks or environments.
Guided Meta-Policy Search
Reinforcement learning (RL) algorithms have demonstrated promising results on complex tasks, yet often require impractical numbers of samples since they learn from scratch.
Meta-Reinforcement Learning of Structured Exploration Strategies
Exploration is a fundamental challenge in reinforcement learning (RL).
