Search Results for author:
Found 9 papers, 6 papers with code
PoCo: Policy Composition from and for Heterogeneous Robot Learning
Training general robotic policies from heterogeneous data for different tasks is a significant challenge.
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data.
Robot Fleet Learning via Policy Merging
We show that FLEET-MERGE consolidates the behavior of policies trained on 50 tasks in the Meta-World environment, with good performance on nearly all training tasks at test time.
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
In this paper, we introduce probabilistic modeling to the inverse graphics framework to quantify uncertainty and achieve robustness in 6D pose estimation tasks.
Ranked #1 on
on YCB-Video
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis
Expert demonstrations are a rich source of supervision for training visual robotic manipulation policies, but imitation learning methods often require either a large number of demonstrations or expensive online expert supervision to learn reactive closed-loop behaviors.
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?
Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems.
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving.
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover system to improve the grasping performance of unseen objects.
Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection
In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem.
Robotics
