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Found 21 papers, 6 papers with code
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples for the target task.
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
Our analysis, both in simulation and in the real world, shows that our approach is the best across data budgets, while standard offline RL from teacher rollouts is surprisingly effective when enough data is given.
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
We study the problem of robotic stacking with objects of complex geometry.
Ranked #2 on
Skill Generalization
on RGB-Stacking
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an autonomous way without human intervention, enabling continual learning under realistic assumptions.
Modular Networks for Compositional Instruction Following
In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type.
Self-Supervised Goal-Conditioned Pick and Place
Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world.
Robotics
Learning to Reach Goals via Iterated Supervised Learning
Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.
Multi-Goal Reinforcement Learning
Reinforcement Learning (RL)
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments
Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche.
Compositional Plan Vectors
We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training.
Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control
We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training.
SMiRL: Surprise Minimizing RL in Entropic Environments
All living organisms struggle against the forces of nature to carve out niches where they can maintain relative stasis.
Unsupervised Pre-training
Unsupervised Reinforcement Learning
Learning to Reach Goals Without Reinforcement Learning
By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic complexities and optimization challenges of reinforcement learning, at the cost of requiring an expert demonstrator -- typically a person -- to provide the demonstrations.
Monocular Plan View Networks for Autonomous Driving
Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth.
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.
Deep Object-Centric Policies for Autonomous Driving
While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways.
Deep Object-Centric Representations for Generalizable Robot Learning
We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy.
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures.
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer
Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy classes, but exacerbates the challenge of data collection, since such methods tend to be less efficient than RL with low-dimensional, hand-designed representations.
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains.
Embedding Word Similarity with Neural Machine Translation
Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model.
Not All Neural Embeddings are Born Equal
Neural language models learn word representations that capture rich linguistic and conceptual information.
