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Found 16 papers, 6 papers with code
Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
PaLM-E: An Embodied Multimodal Language Model
Large language models excel at a wide range of complex tasks.
Ranked #2 on
Visual Question Answering (VQA)
on OK-VQA
Interactive Language: Talking to Robots in Real Time
We present a framework for building interactive, real-time, natural language-instructable robots in the real world, and we open source related assets (dataset, environment, benchmark, and policies).
Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations
Our self-supervised representations are learned by observing the agent freely interacting with different parts of the environment and is queried in two different settings: (i) policy learning and (ii) object location prediction.
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.
Implicit Behavioral Cloning
We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models.
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning
This task space can be quite general and abstract; its only requirements are to be sampleable and to well-cover the space of useful tasks.
Learning to Play by Imitating Humans
Acquiring multiple skills has commonly involved collecting a large number of expert demonstrations per task or engineering custom reward functions.
Language Conditioned Imitation Learning over Unstructured Data
Prior work in imitation learning typically requires each task be specified with a task id or goal image -- something that is often impractical in open-world environments.
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks.
Online Object Representations with Contrastive Learning
We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics.
Wasserstein Dependency Measure for Representation Learning
Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the-art performance in applications such as object recognition, speech recognition, and reinforcement learning.
Learning Latent Plans from Play
Learning from play (LfP) offers three main advantages: 1) It is cheap.
Robotics
Learning Actionable Representations from Visual Observations
In this work we explore a new approach for robots to teach themselves about the world simply by observing it.
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.
Ranked #3 on
Video Alignment
on UPenn Action
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank
As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem.
