DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
Rapid exploration for open- world navigation with latent goal models
7 Pith papers cite this work. Polarity classification is still indexing.
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3D-Belief maintains and updates explicit 3D beliefs about partially observed environments to enable multi-hypothesis imagination and improved performance on embodied tasks.
STRNet improves goal-conditioned visual navigation by replacing simplistic encoders and pooling with a spatio-temporal fusion module that performs spatial graph reasoning and hybrid temporal modeling.
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
FeudalNav decomposes visual navigation into hierarchical levels with a visual-similarity latent memory, delivering competitive Habitat AI results without any odometry.
COMPASS is a manipulation-aware active sensing framework that raises simulated manipulation success rates by 24.25% over information-gain-only baselines in a new four-level confined-space benchmark.
GoViG decomposes goal-conditioned navigation instruction generation into visual state prediction and instruction synthesis using an autoregressive multimodal LLM with one-pass and interleaved reasoning, showing gains on a new R2R-Goal dataset.
citing papers explorer
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Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
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3D-Belief: Embodied Belief Inference via Generative 3D World Modeling
3D-Belief maintains and updates explicit 3D beliefs about partially observed environments to enable multi-hypothesis imagination and improved performance on embodied tasks.
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STRNet: Visual Navigation with Spatio-Temporal Representation through Dynamic Graph Aggregation
STRNet improves goal-conditioned visual navigation by replacing simplistic encoders and pooling with a spatio-temporal fusion module that performs spatial graph reasoning and hybrid temporal modeling.
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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
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FeudalNav: A Simple Framework for Visual Navigation
FeudalNav decomposes visual navigation into hierarchical levels with a visual-similarity latent memory, delivering competitive Habitat AI results without any odometry.
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COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy
COMPASS is a manipulation-aware active sensing framework that raises simulated manipulation success rates by 24.25% over information-gain-only baselines in a new four-level confined-space benchmark.
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GoViG: Goal-Conditioned Visual Navigation Instruction Generation via Multimodal Reasoning
GoViG decomposes goal-conditioned navigation instruction generation into visual state prediction and instruction synthesis using an autoregressive multimodal LLM with one-pass and interleaved reasoning, showing gains on a new R2R-Goal dataset.