49 citations · 194 across the 54 of their papers we have counts for
5 papers · 1 filter
Learning Task Constraints from Demonstration for Hybrid Force/Position Control
Adam Conkey, Tucker Hermans
We present a novel method for learning hybrid force/position control from demonstration. We learn a dynamic constraint frame aligned to the direction of desired force using Cartesi…
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…
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…