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Found 14 papers, 6 papers with code
Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning
For encouraging the agent to well capture the difference brought by perturbation, a perturbation-aware contrastive learning mechanism is further developed by contrasting perturbation-free trajectory encodings and perturbation-based counterparts.
Surfer: Progressive Reasoning with World Models for Robotic Manipulation
To conduct a comprehensive and systematic evaluation of the robot manipulation model in terms of language understanding and physical execution, we also created a robotic manipulation benchmark with progressive reasoning tasks, called SeaWave.
Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration
To improve the ability of fast cross-domain adaptation, we propose Prompt-based Environmental Self-exploration (ProbES), which can self-explore the environments by sampling trajectories and automatically generates structured instructions via a large-scale cross-modal pretrained model (CLIP).
Contrastive Instruction-Trajectory Learning for Vision-Language Navigation
The vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction.
Deep Learning for Embodied Vision Navigation: A Survey
A navigation agent is supposed to have various intelligent skills, such as visual perceiving, mapping, planning, exploring and reasoning, etc.
Vision-Language Navigation with Random Environmental Mixup
Then, we cross-connect the key views of different scenes to construct augmented scenes.
Ranked #38 on
Vision and Language Navigation
on VLN Challenge
SOON: Scenario Oriented Object Navigation with Graph-based Exploration
In this task, an agent is required to navigate from an arbitrary position in a 3D embodied environment to localize a target following a scene description.
Ranked #5 on
Visual Navigation
on SOON Test
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.
Self-Motivated Communication Agent for Real-World Vision-Dialog Navigation
Vision-Dialog Navigation (VDN) requires an agent to ask questions and navigate following the human responses to find target objects.
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.
Vision-Dialog Navigation by Exploring Cross-modal Memory
Benefiting from the collaborative learning of the L-mem and the V-mem, our CMN is able to explore the memory about the decision making of historical navigation actions which is for the current step.
Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks
In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information.
Ranked #13 on
Vision and Language Navigation
on VLN Challenge
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
We first develop an unsupervised diversity exploration method to learn task-specific skills using an unsupervised objective.
Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation
Specifically, our method employs an adversarial feature adaptation model for visual representation transfer and a policy mimic strategy for policy behavior imitation.
