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Found 8 papers, 3 papers with code
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery
We introduce LOTUS, a continual imitation learning algorithm that empowers a physical robot to continuously and efficiently learn to solve new manipulation tasks throughout its lifespan.
Learning Generalizable Manipulation Policies with Object-Centric 3D Representations
We introduce GROOT, an imitation learning method for learning robust policies with object-centric and 3D priors.
Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments
We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive locomotion into high-level decision-making to predict navigation commands and low-level gait generation to realize the target commands.
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation
From the task structures of multi-task demonstrations, we identify skills based on the recurring patterns and train goal-conditioned sensorimotor policies with hierarchical imitation learning.
Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations
The experimental results in simulation and on the real robot have demonstrated that the use of implicit neural representations and joint learning of grasp affordance and 3D reconstruction have led to state-of-the-art grasping results.
Fast Uncertainty Quantification for Deep Object Pose Estimation
Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transfer.
Machine versus Human Attention in Deep Reinforcement Learning Tasks
1) How similar are the visual representations learned by RL agents and humans when performing the same task?
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine.
