262 citations · 430 across the 32 of their papers we have counts for
6 papers · 1 filter
Learning Latent Space Dynamics for Tactile Servoing
Giovanni Sutanto, Nathan Ratliff, Balakumar Sundaralingam +4
To achieve a dexterous robotic manipulation, we need to endow our robot with tactile feedback capability, i.e. the ability to drive action based on tactile sensing. In this paper,…
Robust Learning of Tactile Force Estimation through Robot Interaction
Balakumar Sundaralingam, Alexander Lambert, Ankur Handa +5
Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust…
Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
Jonathan Tremblay, Thang To, Balakumar Sundaralingam +3
Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out o…
Relaxed-Rigidity Constraints: Kinematic Trajectory Optimization and Collision Avoidance for In-Grasp Manipulation
Balakumar Sundaralingam, Tucker Hermans
This paper proposes a novel approach to performing in-grasp manipulation: the problem of moving an object with reference to the palm from an initial pose to a goal pose without bre…
Geometric In-Hand Regrasp Planning: Alternating Optimization of Finger Gaits and In-Grasp Manipulation
Balakumar Sundaralingam, Tucker Hermans
This paper explores the problem of autonomous, in-hand regrasping--the problem of moving from an initial grasp on an object to a desired grasp using the dexterity of a robot's fing…
Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Network
Qingkai Lu, Kautilya Chenna, Balakumar Sundaralingam +1
We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. We train a convolutional neural network to predict grasp success as a fu…