6 accepted papers
We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike existing datasets limited to merely hundreds or thousands of examples, PhysInOne provides 2 million videos across 153,810 dynamic 3D scenes, covering 71
In this work, we propose A2DIO, a novel hybrid neural network model with a set of carefully designed attention mechanisms for pose invariant inertial odometry. The key idea is to extract both local and global features from the window of IMU measurements for velocity prediction. A2DIO leverages the c…
Grasp pose detection generates the position and orientation of the robot end-effector to grasp objects from the RGB or RGB-D image. In this paper, we propose a novel grasp pose detection network that generates 3-DOF grasp poses using the RGB image. The network follows the anchor-based object detecti…
Learning-based grasp pose detection algorithms have boosted the performance of robot grasping, but they usually need manually fine-tuning steps to find the balance between detection accuracy and efficient. In this paper, we discard these intermediate procedures, like sampling grasps and generating g…
Inertial Measurement Unit (IMU) has long been a dream for stable and reliable motion estimation, especially in indoor environments where GPS strength limits. In this paper, we propose a novel method for position and orientation estimation of a moving object only from a sequence of IMU signals collec…
In this paper, we propose a novel algorithm that estimates the pose of the robot end effector using depth vision. The input to our system is the segmented robot hand point cloud from a depth sensor. Then a neural network takes a point cloud as input and outputs the position and orientation of the ro…