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Found 31 papers, 12 papers with code
Digital Twin Generation from Visual Data: A Survey
This survey explores recent developments in generating digital twins from videos.
STEVE-Audio: Expanding the Goal Conditioning Modalities of Embodied Agents in Minecraft
Recently, the STEVE-1 approach has been introduced as a method for training generative agents to follow instructions in the form of latent CLIP embeddings.
SplatR : Experience Goal Visual Rearrangement with 3D Gaussian Splatting and Dense Feature Matching
In this work, we present a novel framework that leverages on 3D Gaussian Splatting as a 3D scene representation for experience goal visual rearrangement task.
Object and Contact Point Tracking in Demonstrations Using 3D Gaussian Splatting
This paper introduces a method to enhance Interactive Imitation Learning (IIL) by extracting touch interaction points and tracking object movement from video demonstrations.
Towards Open-World Mobile Manipulation in Homes: Lessons from the Neurips 2023 HomeRobot Open Vocabulary Mobile Manipulation Challenge
In order to develop robots that can effectively serve as versatile and capable home assistants, it is crucial for them to reliably perceive and interact with a wide variety of objects across diverse environments.
Video Diffusion Models: A Survey
Diffusion generative models have recently become a powerful technique for creating and modifying high-quality, coherent video content.
Lane Segmentation Refinement with Diffusion Models
The lane graph is a key component for building high-definition (HD) maps and crucial for downstream tasks such as autonomous driving or navigation planning.
Cognitive Planning for Object Goal Navigation using Generative AI Models
Our approach enables a robot to navigate unfamiliar environments by leveraging LLMs and LVLMs to understand the semantic structure of the scene.
Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs
We demonstrate experimental results with LLMs that address robotics task planning problems.
Zero-shot Imitation Policy via Search in Demonstration Dataset
Behavioral cloning uses a dataset of demonstrations to learn a policy.
Benchmarks for Physical Reasoning AI
Therefore, we aim to offer an overview of existing benchmarks and their solution approaches and propose a unified perspective for measuring the physical reasoning capacity of AI systems.
UniTeam: Open Vocabulary Mobile Manipulation Challenge
This report introduces our UniTeam agent - an improved baseline for the "HomeRobot: Open Vocabulary Mobile Manipulation" challenge.
Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks
Reinforcement learning and Imitation Learning approaches utilize policy learning strategies that are difficult to generalize well with just a few examples of a task.
Behavioral Cloning via Search in Embedded Demonstration Dataset
Actions from a selected similar situation can be performed by the agent until representations of the agent's current situation and the selected experience diverge in the latent space.
Contrastive Language, Action, and State Pre-training for Robot Learning
In this paper, we introduce a method for unifying language, action, and state information in a shared embedding space to facilitate a range of downstream tasks in robot learning.
Shape complexity estimation using VAE
In this paper, we compare methods for estimating the complexity of two-dimensional shapes and introduce a method that exploits reconstruction loss of Variational Autoencoders with different sizes of latent vectors.
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022.
Stroke-based Rendering: From Heuristics to Deep Learning
In the last few years, artistic image-making with deep learning models has gained a considerable amount of traction.
Behavioral Cloning via Search in Video PreTraining Latent Space
Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.
Face Generation and Editing with StyleGAN: A Survey
Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN.
Planning with RL and episodic-memory behavioral priors
The practical application of learning agents requires sample efficient and interpretable algorithms.
Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space
The agent is required to pass the previously unknown F1-style track in the minimum time with the least amount of off-road driving violations.
Faces: AI Blitz XIII Solutions
AI Blitz XIII Faces challenge hosted on www. aicrowd. com platform consisted of five problems: Sentiment Classification, Age Prediction, Mask Prediction, Face Recognition, and Face De-Blurring.
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
A Graph-based U-Net Model for Predicting Traffic in unseen Cities
Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow.
YOLO -- You only look 10647 times
With this work we are explaining the "You Only Look Once" (YOLO) single-stage object detection approach as a parallel classification of 10647 fixed region proposals.
Critic Guided Segmentation of Rewarding Objects in First-Person Views
This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset.
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
Solving Physics Puzzles by Reasoning about Paths
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal.
Modularization of End-to-End Learning: Case Study in Arcade Games
Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches.
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
