Search Results for author:
Found 12 papers, 6 papers with code
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning
we formulate the dynamic grasping problem as a 'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it.
$ \text{T}^3 $OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area
Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games.
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
Policy optimization methods are one of the most widely used classes of Reinforcement Learning (RL) algorithms.
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape.
A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles
This paper proposes a Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) that integrates SL into the FDL framework.
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
We also obtain a new upper bound for conservative low-rank MDP.
A Multi-intersection Vehicular Cooperative Control based on End-Edge-Cloud Computing
In particular, we propose a Multi-intersection Vehicular Cooperative Control (MiVeCC) to enable cooperation among vehicles in a large area with multiple unsignalized intersections.
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
In this paper, we conduct sanity checks for the above beliefs on several recent unstructured pruning methods and surprisingly find that: (1) A set of methods which aims to find good subnetworks of the randomly-initialized network (which we call "initial tickets"), hardly exploits any information from the training data; (2) For the pruned networks obtained by these methods, randomly changing the preserved weights in each layer, while keeping the total number of preserved weights unchanged per layer, does not affect the final performance.
Light Field Image Super-Resolution Using Deformable Convolution
In this paper, we propose a deformable convolution network (i. e., LF-DFnet) to handle the disparity problem for LF image SR.
DeOccNet: Learning to See Through Foreground Occlusions in Light Fields
In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet).
