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Found 25 papers, 7 papers with code
Human Action Recognition Based on Spatial-Temporal Attention
Many state-of-the-art methods of recognizing human action are based on attention mechanism, which shows the importance of attention mechanism in action recognition.
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations
Learning from few demonstrations to develop policies robust to variations in robot initial positions and object poses is a problem of significant practical interest in robotics.
Playing Non-Embedded Card-Based Games with Reinforcement Learning
Developing complex non-embedded agents remains challenging, especially in card-based RTS games with complex features and large state spaces.
Bootstrapped Model Predictive Control
In this work, we introduce Bootstrapped Model Predictive Control (BMPC), a novel algorithm that performs policy learning in a bootstrapped manner.
Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation
Decision Transformer (DT) can learn effective policy from offline datasets by converting the offline reinforcement learning (RL) into a supervised sequence modeling task, where the trajectory elements are generated auto-regressively conditioned on the return-to-go (RTG). However, the sequence modeling learning approach tends to learn policies that converge on the sub-optimal trajectories within the dataset, for lack of bridging data to move to better trajectories, even if the condition is set to the highest RTG. To address this issue, we introduce Diffusion-Based Trajectory Branch Generation (BG), which expands the trajectories of the dataset with branches generated by a diffusion model. The trajectory branch is generated based on the segment of the trajectory within the dataset, and leads to trajectories with higher returns. We concatenate the generated branch with the trajectory segment as an expansion of the trajectory. After expanding, DT has more opportunities to learn policies to move to better trajectories, preventing it from converging to the sub-optimal trajectories. Empirically, after processing with BG, DT outperforms state-of-the-art sequence modeling methods on D4RL benchmark, demonstrating the effectiveness of adding branches to the dataset without further modifications.
Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps
Humor is a culturally nuanced aspect of human language that presents challenges for understanding and generation, requiring participants to possess good creativity and strong associative thinking.
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
The simulator is a world model that separately learns dynamics and reward, where the dynamics model comprises an image tokenizer as well as a causal transformer to generate interaction transitions autoregressively, and the reward model is a bidirectional transformer learned by maximizing the likelihood of trajectories in the expert demonstrations under language guidance.
Relation DETR: Exploring Explicit Position Relation Prior for Object Detection
The experimental results on the dataset illustrate that the proposed explicit position relation achieves a clear improvement of 1. 3% AP, highlighting its potential towards universal object detection.
Ranked #1 on
Object Detection
on SA-Det-100k
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios.
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
We propose a novel Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue.
MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes
Manipulation relationship detection (MRD) aims to guide the robot to grasp objects in the right order, which is important to ensure the safety and reliability of grasping in object stacked scenes.
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning
To ensure optimal consistency, the optimal node is required to be the unique STN.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning (MARL) methods with linear or monotonic value decomposition can not ensure the optimal consistency (i. e. the correspondence between the individual greedy actions and the maximal true Q value), leading to instability and poor coordination.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection
Fusing the two kinds of data usually helps to improve the detection results.
Probabilistic Human Motion Prediction via A Bayesian Neural Network
The experimental results show that our approach makes the interaction more efficient and safer.
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter
In this paper, we present a new dataset named REGRAD for the learning of relationships among objects and grasps.
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation
General methods for policy based multi-agent reinforcement learning to solve the challenge introduce differentiate value functions or advantage functions for individual agents.
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning
Using a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem.
REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds
Reliable robotic grasping in unstructured environments is a crucial but challenging task.
Robotics
Hindsight Trust Region Policy Optimization
We propose \emph{Hindsight Trust Region Policy Optimization}(HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with \emph{hindsight} to tackle the challenge of sparse rewards.
A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes
Autonomous robotic grasping plays an important role in intelligent robotics.
Robotics
A Real-time Robotic Grasp Approach with Oriented Anchor Box
The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors.
Robotics
ROI-based Robotic Grasp Detection for Object Overlapping Scenes
Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset.
Robotics
Fully Convolutional Grasp Detection Network with Oriented Anchor Box
The feature extractor is a deep convolutional neural network.
