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Found 15 papers, 8 papers with code
TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft
Collaboration is a cornerstone of society.
Low Latency Point Cloud Rendering with Learned Splatting
Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation.
Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering
Point cloud is a promising 3D representation for volumetric streaming in emerging AR/VR applications.
Position Paper: Agent AI Towards a Holistic Intelligence
Recent advancements in large foundation models have remarkably enhanced our understanding of sensory information in open-world environments.
An Interactive Agent Foundation Model
We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks.
Agent AI: Surveying the Horizons of Multimodal Interaction
To accelerate research on agent-based multimodal intelligence, we define "Agent AI" as a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data, and can produce meaningful embodied actions.
MindAgent: Emergent Gaming Interaction
Large Language Models (LLMs) have the capacity of performing complex scheduling in a multi-agent system and can coordinate these agents into completing sophisticated tasks that require extensive collaboration.
LEMMA: Learning Language-Conditioned Multi-Robot Manipulation
We introduce a benchmark for LanguagE-Conditioned Multi-robot MAnipulation (LEMMA) focused on task allocation and long-horizon object manipulation based on human language instructions in a tabletop setting.
ArK: Augmented Reality with Knowledge Interactive Emergent Ability
In this study, we develop an infinite agent that learns to transfer knowledge memory from general foundation models (e. g. GPT4, DALLE) to novel domains or scenarios for scene understanding and generation in the physical or virtual world.
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes.
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively.
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS).
Ranked #1 on
Mathematical Question Answering
on GeoS
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability.
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state.
Bayesian Inference
Explainable Artificial Intelligence (XAI)
+1
VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
One of the main challenges of advancing task-oriented learning such as visual task planning and reinforcement learning is the lack of realistic and standardized environments for training and testing AI agents.
