20 citations · 150 across the 43 of their papers we have counts for
7 papers · 1 filter
Imitation and Supervised Learning of Compliance for Robotic Assembly
Devesh K. Jha, Diego Romeres, William Yerazunis +1
We present the design of a learning-based compliance controller for assembly operations for industrial robots. We propose a solution within the general setting of learning from dem…
Distributed Task Allocation in Homogeneous Swarms Using Language Measure Theory
Devesh K. Jha
In this paper, we present algorithms for synthesizing controllers to distribute a group (possibly swarms) of homogeneous robots (agents) over heterogeneous tasks which are operated…
Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units
Shiyu Jin, Diego Romeres, Arvind Ragunathan +2
This paper presents a novel trajectory optimization formulation to solve the robotic assembly of the belt drive unit. Robotic manipulations involving contacts and deformable object…
PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance
Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres
PYROBOCOP is a lightweight Python-based package for control and optimization of robotic systems described by nonlinear Differential Algebraic Equations (DAEs). In particular, the p…
Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
Siyuan Dong, Devesh K. Jha, Diego Romeres +3
Object insertion is a classic contact-rich manipulation task. The task remains challenging, especially when considering general objects of unknown geometry, which significantly lim…
Markov Modeling of Time-Series Data using Symbolic Analysis
Devesh K. Jha
Markov models are often used to capture the temporal patterns of sequential data for statistical learning applications. While the Hidden Markov modeling-based learning mechanisms a…
Training Larger Networks for Deep Reinforcement Learning
Kei Ota, Devesh K. Jha, Asako Kanezaki
The success of deep learning in the computer vision and natural language processing communities can be attributed to training of very deep neural networks with millions or billions…