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Found 20 papers, 10 papers with code
Multi-Game Decision Transformers
Specifically, we show that a single transformer-based model - with a single set of weights - trained purely offline can play a suite of up to 46 Atari games simultaneously at close-to-human performance.
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.
Bayesian Imitation Learning for End-to-End Mobile Manipulation
In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a mobile manipulator.
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning.
Practical Imitation Learning in the Real World via Task Consistency Loss
To the best of our knowledge, this is the first work to tackle latched door opening from a purely end-to-end learning approach, where the task of navigation and manipulation are jointly modeled by a single neural network.
Meta-Learning Requires Meta-Augmentation
Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that quickly updates that model when given examples from a new task.
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Imitation learning allows agents to learn complex behaviors from demonstrations.
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action.
Scalable Multi-Task Imitation Learning with Autonomous Improvement
In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation.
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Imitation learning allows agents to learn complex behaviors from demonstrations.
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
We formulate an arithmetic relationship between feature vectors from this observation, and use it to learn a representation of scenes and objects that can then be used to identify object instances, localize them in the scene, and perform goal-directed grasping tasks where the robot must retrieve commanded objects from a bin.
WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
Machine learning models encounter Out-of-Distribution (OoD) errors when the data seen at test time are generated from a different stochastic generator than the one used to generate the training data.
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach.
Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control
In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback.
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping.
Sim2Real View Invariant Visual Servoing by Recurrent Control
To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object.
End-to-End Learning of Semantic Grasping
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images.
Time-Contrastive Networks: Self-Supervised Learning from Video
While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human.
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Categorical Reparameterization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete structure in the world.
Categorical Reparametrization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete structure in the world.
