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Found 32 papers, 7 papers with code
Scale-Invariant Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
This novel transformation process ensures that the converted constraints are invariant to the objective scales while having the same effect as the original objectives.
Multi-Objective Reinforcement Learning
reinforcement-learning
Diffused Task-Agnostic Milestone Planner
To this end, we propose a method to utilize a diffusion-based generative sequence model to plan a series of milestones in a latent space and to have an agent to follow the milestones to accomplish a given task.
TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning
In this paper, we propose a trust region-based safe RL method with CVaR constraints, called TRC.
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk
This paper aims to solve a safe reinforcement learning (RL) problem with risk measure-based constraints.
Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding
We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state.
Ranked #1 on
Visual Navigation
on R2R
Renderable Neural Radiance Map for Visual Navigation
We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment.
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints must be met, such as avoiding collisions, limiting energy consumption, and maintaining balance.
Distributional Reinforcement Learning
reinforcement-learning
+2
Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations
To provide sufficient information for estimating a complete texture map, the proposed model simultaneously generates multi-view hallucinations in the image domain and an estimated texture map in the texture domain.
Visual Graph Memory With Unsupervised Representation for Visual Navigation
We present a novel graph-structured memory for visual navigation, called visual graph memory (VGM), which consists of unsupervised image representations obtained from navigation history.
Visually Grounding Language Instruction for History-Dependent Manipulation
We also suggest a relevant dataset and model which can be a baseline, and show that our model trained with the proposed dataset can also be applied to the real world based on the CycleGAN.
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards
In simulation, the proposed estimator shows favorable performance compared to existing robust estimators for various $p$ values and, for MAB problems, the proposed perturbation strategy outperforms existing exploration methods.
Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music
The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 5, 000 pose frames.
Deep Elastic Networks with Model Selection for Multi-Task Learning
To this end, we propose an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks.
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
To this end, we propose a novel network architecture producing multiple networks of different configurations, termed deep virtual networks (DVNs), for different tasks.
Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning
By controlling the entropic index, we can generate various types of entropy, including the SG entropy, and a different entropy results in a different class of the optimal policy in Tsallis MDPs.
Learning Instance-Aware Object Detection Using Determinantal Point Processes
Recent object detectors find instances while categorizing candidate regions.
Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration
To handle the inherent ambiguity in human language commands, a suitable question which can resolve the ambiguity is generated.
Maximum Causal Tsallis Entropy Imitation Learning
Third, we propose a maximum causal Tsallis entropy imitation learning (MCTEIL) algorithm with a sparse mixture density network (sparse MDN) by modeling mixture weights using a sparsemax distribution.
Deep Pose Consensus Networks
In this paper, to resolve this issue, we propose a multiple-partial-hypothesis-based framework for the problem of estimating 3D human pose from a single image, which can be fine-tuned in an end-to-end fashion.
Generative Single Image Reflection Separation
Single image reflection separation is an ill-posed problem since two scenes, a transmitted scene and a reflected scene, need to be inferred from a single observation.
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning.
Text2Action: Generative Adversarial Synthesis from Language to Action
We demonstrate that the network can generate human-like actions which can be transferred to a Baxter robot, such that the robot performs an action based on a provided sentence.
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning
The proposed sparse MDP is compared to soft MDPs which utilize causal entropy regularization.
Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling
The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset.
Unsupervised Holistic Image Generation from Key Local Patches
We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior.
Density Matching Reward Learning
The performance of KDMRL is extensively evaluated in two sets of experiments: grid world and track driving experiments.
Consensus of Non-Rigid Reconstructions
Recently, there have been many progresses for the problem of non-rigid structure reconstruction based on 2D trajectories, but it is still challenging to deal with complex deformations or restricted view ranges.
Fast and Accurate Head Pose Estimation via Random Projection Forests
In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image.
Elastic-Net Regularization of Singular Values for Robust Subspace Learning
The proposed method is applied to a number of low-rank matrix approximation problems to demonstrate its efficiency in the presence of heavy corruptions and to show its effectiveness and robustness compared to the existing methods.
A Procrustean Markov Process for Non-Rigid Structure Recovery
Recovering a non-rigid 3D structure from a series of 2D observations is still a difficult problem to solve accurately.
Procrustean Normal Distribution for Non-rigid Structure from Motion
Non-rigid structure from motion is a fundamental problem in computer vision, which is yet to be solved satisfactorily.
Markov Chain Monte Carlo Data Association for Multiple-Target Tracking
This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multiple-target tracking in a cluttered environment.
