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Found 13 papers, 5 papers with code
DNAct: Diffusion Guided Multi-Task 3D Policy Learning
To learn a generalizable multi-task policy with few demonstrations, the pre-training phase of DNAct leverages neural rendering to distill 2D semantic features from foundation models such as Stable Diffusion to a 3D space, which provides a comprehensive semantic understanding regarding the scene.
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
To incorporate semantics in 3D, the reconstruction module utilizes a vision-language foundation model ($\textit{e. g.}$, Stable Diffusion) to distill rich semantic information into the deep 3D voxel.
Elastic Decision Transformer
This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants.
Learning Generalizable Dexterous Manipulation from Human Grasp Affordance
In this paper, we propose to learn dexterous manipulation using large-scale demonstrations with diverse 3D objects in a category, which are generated from a human grasp affordance model.
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation.
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization
Specifically, we are required to provide a solution that is able to (1) handle the traffic signal control when certain surveillance cameras that retrieve information for reinforcement learning are down, (2) learn from batch data without a traffic simulator, and (3) make control decisions without shared information across intersections.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
Ranked #673 on
Image Classification
on ImageNet
Model Imitation for Model-Based Reinforcement Learning
Based on the claim, we propose to learn the transition model by matching the distributions of multi-step rollouts sampled from the transition model and the real ones via WGAN.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning
If artificially intelligent (AI) agents make decisions on behalf of human beings, we would hope they can also follow established regulations while interacting with humans or other AI agents.
Imitation Learning from Imperfect Demonstration
Imitation learning (IL) aims to learn an optimal policy from demonstrations.
A Memory-Network Based Solution for Multivariate Time-Series Forecasting
Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting.
ANS: Adaptive Network Scaling for Deep Rectifier Reinforcement Learning Models
This work provides a thorough study on how reward scaling can affect performance of deep reinforcement learning agents.
A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents
This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically.
