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Found 15 papers, 5 papers with code
DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots
We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three-fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation.
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.
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner
Imitation learning circumvents this problem and has been used with motion capture data to extract quadruped gaits for flat terrains.
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
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
In this work, we learn a latent state representation implicitly with deep reinforcement learning in simulation, and then adapt it to the real domain using unlabeled real robot data.
Off-Policy Evaluation via Off-Policy Classification
However, for high-dimensional observations, such as images, models of the environment can be difficult to fit and value-based methods can make IS hard to use or even ill-conditioned, especially when dealing with continuous action spaces.
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks
Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power.
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain.
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
Generative Adversarial Network
Unsupervised Domain Adaptation
Domain Separation Networks
However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.
Ranked #1 on
Domain Adaptation
on Synth Objects-to-LINEMOD
A deep matrix factorization method for learning attribute representations
Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation.
