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Found 21 papers, 4 papers with code
Yell At Your Robot: Improving On-the-Fly from Language Corrections
In this paper, we make the following observation: high-level policies that index into sufficiently rich and expressive low-level language-conditioned skills can be readily supervised with human feedback in the form of language corrections.
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback.
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Instead, our approach BOSS (BOotStrapping your own Skills) learns to accomplish new tasks by performing "skill bootstrapping," where an agent with a set of primitive skills interacts with the environment to practice new skills without receiving reward feedback for tasks outside of the initial skill set.
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data.
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling
Pre-training robot policies with a rich set of skills can substantially accelerate the learning of downstream tasks.
Cross-Domain Transfer via Semantic Skill Imitation
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 domain, e. g. a robotic manipulator in a simulated kitchen.
RT-1: Robotics Transformer for Real-World Control at Scale
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance.
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research.
Skill-based Meta-Reinforcement Learning
While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real robot systems infeasible.
Task-Induced Representation Learning
We investigate the effectiveness of unsupervised and task-induced representation learning approaches on four visually complex environments, from Distracting DMControl to the CARLA driving simulator.
Demonstration-Guided Reinforcement Learning with Learned Skills
Prior approaches for demonstration-guided RL treat every new task as an independent learning problem and attempt to follow the provided demonstrations step-by-step, akin to a human trying to imitate a completely unseen behavior by following the demonstrator's exact muscle movements.
Minimum Description Length Skills for Accelerated Reinforcement Learning
Humans can quickly learn new tasks by reusing a large number of previously acquired skills.
Accelerating Reinforcement Learning with Learned Skill Priors
We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets.
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment.
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
In this work we propose a framework for visual prediction and planning that is able to overcome both of these limitations.
Keyframing the Future: Discovering Temporal Hierarchy with Keyframe-Inpainter Prediction
To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed.
Goal-Conditioned Video Prediction
Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video.
Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning
We propose a model that learns to discover these important events and the times when they occur and uses them to represent the full sequence.
Learning what you can do before doing anything
We introduce a loss term that encourages the network to capture the composability of visual sequences and show that it leads to representations that disentangle the structure of actions.
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded.
