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Found 16 papers, 8 papers with code
Open-World Skill Discovery from Unsegmented Demonstrations
Unlike existing methods that rely on sequence sampling or human labeling, we have developed a self-supervised learning-based approach to segment these long videos into a series of semantic-aware and skill-consistent segments.
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
We aim to develop a goal specification method that is semantically clear, spatially sensitive, and intuitive for human users to guide agent interactions in embodied environments.
MineStudio: A Streamlined Package for Minecraft AI Agent Development
Minecraft has emerged as a valuable testbed for embodied intelligence and sequential decision-making research, yet the development and validation of novel agents remains hindered by significant engineering challenges.
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents
Developing agents that can follow multimodal instructions remains a fundamental challenge in robotics and AI.
Optimizing Latent Goal by Learning from Trajectory Preference
Then we use preference learning to fine-tune the initial goal latent representation with the categorized trajectories while keeping the policy backbone frozen.
ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context Prompting
Using this approach, we train ROCKET-1, a low-level policy that predicts actions based on concatenated visual observations and segmentation masks, supported by real-time object tracking from SAM-2.
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents
First, we introduce a self-supervised approach to learn a behavior encoder that produces discretized tokens for behavior trajectories $\tau = \{o_0, a_0, \dots\}$ and an imitation learning policy decoder conditioned on these tokens.
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents.
GROOT: Learning to Follow Instructions by Watching Gameplay Videos
We propose to follow reference videos as instructions, which offer expressive goal specifications while eliminating the need for expensive text-gameplay annotations.
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
We investigate the challenge of task planning for multi-task embodied agents in open-world environments.
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction
We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents.
Automatic Relation-aware Graph Network Proliferation
However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information.
Ranked #2 on
Link Prediction
on TSP/HCP Benchmark set
IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning
Conditional image generation is an active research topic including text2image and image translation.
Conditional Image Generation
Generative Adversarial Network
+1
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing
To address these limitations, we design a Dynamic Style Manipulation Network (DyStyle) whose structure and parameters vary by input samples, to perform nonlinear and adaptive manipulation of latent codes for flexible and precise attribute control.
Edge-featured Graph Neural Architecture Search
Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges.
Rethinking Graph Neural Architecture Search from Message-passing
Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space.
