activity
20172022
most citedTraining Larger Networks for Deep Reinforcement Learning

10 citations · 62 across the 20 of their papers we have counts for

collaborators

31 papers

cs.RO2022

Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control

Devesh K. Jha, Siddarth Jain, Diego Romeres +2

Robots have been steadily increasing their presence in our daily lives, where they can work along with humans to provide assistance in various tasks on industry floors, in offices,…

cs.RO2022

Active Exploration for Robotic Manipulation

Tim Schneider, Boris Belousov, Georgia Chalvatzaki +3

Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years. One of the key challenges in manipulation i…

cs.RO20225 cited

Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills

Seiji Shaw, Devesh K. Jha, Arvind Raghunathan +4

Dynamic movement primitives are widely used for learning skills which can be demonstrated to a robot by a skilled human or controller. While their generalization capabilities and s…

cs.RO2022

Design of Adaptive Compliance Controllers for Safe Robotic Assembly

Devesh K. Jha, Diego Romeres, Siddarth Jain +2

Insertion operations are a critical element of most robotic assembly operation, and peg-in-hole (PiH) insertion is one of the most widely studied tasks in the industrial and academ…

cs.RO2022

PYROBOCOP: Python-based Robotic Control & Optimization Package for Manipulation

Arvind Raghunathan, Devesh K. Jha, Diego Romeres

PYROBOCOP is a Python-based package for control, optimization and estimation of robotic systems described by nonlinear Differential Algebraic Equations (DAEs). In particular, the p…

cs.RO20227 cited

Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulation

Yuki Shirai, Devesh K. Jha, Arvind Raghunathan +1

This paper presents a chance-constrained formulation for robust trajectory optimization during manipulation. In particular, we present a chance-constrained optimization for Stochas…