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Found 24 papers, 5 papers with code
Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction
Integrating robots into populated environments is a complex challenge that requires an understanding of human social dynamics.
HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs
In this paper, we propose a Human-Object Interaction (HOI) anticipation framework for collaborative robots.
Ranked #1 on
Human-Object Interaction Anticipation
on VidHOI
Human-Object Interaction Anticipation
Human-Object Interaction Detection
ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space
Additionally, we propose a consistency term to build a common latent space that captures the similarity of the poses with precision while allowing direct robot motion control from the latent space.
A Unified Masked Autoencoder with Patchified Skeletons for Motion Synthesis
Experimental results show that our model successfully forecasts human motion on the Human3. 6M dataset.
Human-Object Interaction Prediction in Videos through Gaze Following
Understanding the human-object interactions (HOIs) from a video is essential to fully comprehend a visual scene.
Ranked #2 on
Human-Object Interaction Anticipation
on VidHOI
Human-Object Interaction Anticipation
Human-Object Interaction Detection
+1
Input-Output Feedback Linearization Preserving Task Priority for Multivariate Nonlinear Systems Having Singular Input Gain Matrix
The key observation is that the usual input-output linearization problem can be interpreted as the problem of solving simultaneous linear equations associated with the input gain matrix: thus, even at points where the input gain matrix becomes singular, it is still possible to solve a part of linear equations, by which a subset of input-output relations is made linear or close to be linear.
Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?
After many researchers observed fruitfulness from the recent diffusion probabilistic model, its effectiveness in image generation is actively studied these days.
Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking
In this work, we develop a multi-modality tracker that fuses information from visual appearance and geometry to estimate object poses.
Ranked #1 on
6D Pose Estimation
on YCB-Video
Robust Human Motion Forecasting using Transformer-based Model
In addition, our model is tested in conditions where the human motion is severely occluded, demonstrating its robustness in reconstructing and predicting 3D human motion in a highly noisy environment.
Intention-Conditioned Long-Term Human Egocentric Action Forecasting
Our framework first extracts two level of human information over the N observed videos human actions through a Hierarchical Multi-task MLP Mixer (H3M).
Ranked #1 on
Long Term Action Anticipation
on Ego4D
Long-Horizon Planning and Execution with Functional Object-Oriented Networks
We demonstrate our entire approach on long-horizon tasks in CoppeliaSim and show how learned action contexts can be extended to never-before-seen scenarios.
A Road-map to Robot Task Execution with the Functional Object-Oriented Network
Following work on joint object-action representations, the functional object-oriented network (FOON) was introduced as a knowledge graph representation for robots.
Refining Action Segmentation With Hierarchical Video Representations
In this paper, we propose Hierarchical Action Segmentation Refiner (HASR), which can refine temporal action segmentation results from various models by understanding the overall context of a given video in a hierarchical way.
Ranked #12 on
Action Segmentation
on 50 Salads
Visually Grounding Language Instruction for History-Dependent Manipulation
We also suggest a relevant dataset and model which can be a baseline, and show that our model trained with the proposed dataset can also be applied to the real world based on the CycleGAN.
Efficient State Abstraction using Object-centered Predicates for Manipulation Planning
To tackle these limitations we propose an object-centered representation that permits characterizing a much wider set of possible changes in configuration spaces than the traditional observer perspective counterpart.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy
In this paper, a motion capturing algorithm is proposed for upper body motion tracking.
A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration
In this work, we introduce the problem of cross-modal visuo-tactile object recognition with robotic active exploration.
A Human Action Descriptor Based on Motion Coordination
The second step enriches the descriptor considering minimum and maximum joint velocities and the correlations between the most informative joints.
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation
This way, the occurrence of object slipping during the learning procedure, which we consider an irreversible event, is significantly reduced.
Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer
In addition to the pose estimation task, the voting-based scheme can also provide point cloud segmentation result without ground-truth for segmentation.
Model-based Hand Pose Estimation for Generalized Hand Shape with Appearance Normalization
In this work, we extend the kinematic layer to make the hand shape parameters learnable.
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
Ranked #5 on
Hand Pose Estimation
on HANDS 2017
A Preliminary Study on the Learning Informativeness of Data Subsets
We prove the concept on human-written texts, and conjecture this work will reduce training data size of sequential instructions, while preserving semantic relations, when gathering information from large remote sources.
