Advancements in Dexterous Manipulation and Human-Object Interaction

The field of robotics and computer vision is witnessing significant advancements in dexterous manipulation and human-object interaction. Recent research has focused on developing novel methods for efficient transfer of human bimanual skills to robotic hands, grasp planning, and force estimation. These innovations have the potential to revolutionize various applications, including robotics, augmented reality, and assistive technologies. Notably, the integration of physical reasoning and constraints into pose estimation and grasp planning has shown promising results, enabling more accurate and robust performance in real-world scenarios. Furthermore, the development of datasets and benchmarks, such as DexManipNet and BOP-H3, has facilitated the evaluation and comparison of different methods, driving progress in the field.

Some noteworthy papers in this area include ManipTrans, which introduces a novel two-stage method for transferring human bimanual skills to dexterous robotic hands, and ForcePose, which proposes a deep learning framework for estimating applied forces based on action recognition and object detection. Additionally, the BOP Challenge 2024 has provided a comprehensive evaluation of the state-of-the-art in 6D object pose estimation, highlighting the advancements in model-based and model-free methods.

Sources

ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning

Bimanual Regrasp Planning and Control for Eliminating Object Pose Uncertainty

ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection

Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation

Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks

Collapse and Collision Aware Grasping for Cluttered Shelf Picking

SIGHT: Single-Image Conditioned Generation of Hand Trajectories for Hand-Object Interaction

FRAME: Floor-aligned Representation for Avatar Motion from Egocentric Video

Learning Predictive Visuomotor Coordination

Dexterous Non-Prehensile Manipulation for Ungraspable Object via Extrinsic Dexterity

Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models

Enhancing Human Motion Prediction via Multi-range Decoupling Decoding with Gating-adjusting Aggregation

PhysPose: Refining 6D Object Poses with Physical Constraints

Investigation of intelligent barbell squat coaching system based on computer vision and machine learning

Learning Velocity and Acceleration: Self-Supervised Motion Consistency for Pedestrian Trajectory Prediction

Gaze-Guided 3D Hand Motion Prediction for Detecting Intent in Egocentric Grasping Tasks

Direction-Aware Hybrid Representation Learning for 3D Hand Pose and Shape Estimation

Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments

A Planning Framework for Stable Robust Multi-Contact Manipulation

PicoPose: Progressive Pixel-to-Pixel Correspondence Learning for Novel Object Pose Estimation

BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation