Inertial Properties in Robotic Manipulation: An Object-Level Framework

By Oussama Khatib. 1995

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Citations in the corpus

robosuite: A Modular Simulation Framework and Benchmark for Robot Learning (2020, background): “For example, the Operational Space Controllers [6] interpret high-level actions as desired end-effector configurations, either positions (three degrees of freedom) or poses (six degrees of freedom), and compute the corresponding joint torques to move the robot’s end-effector from its current to the ”
Robot dynamics: equations and algorithms (2000, background): “This formulation has been particularly successful in hybrid motion/force control and related applications [21].”
Human–Robot Collaboration Based on Motion Intention Estimation (2014, background): “More deta ils can be found in [28], [29].”
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments (2023, methodology): “With ORBIT, we include GPU-based implementations for: differential IK [37], operational-space control [38] and jointlevel control.”
Learning variable impedance control (2011, methodology): “First by determining the configuration at a given point in the task, the apparent inertia at the end-effector is co-determined (Hogan 1985b; Khatib 1995; Sciavicco and Siciliano 2000b), and second by possibly purposefully using a reference trajectory which is far from the actually followed…”
Elastic Strips: A Framework for Motion Generation in Human Environments (2002, methodology): “The approach to motion coordination for robots with kinematically complex structures presented in this paper relies on the operational space formulation (Khatib 1987), using the dynamically consistent force/torque relationship ( Khatib 1995 ) to control the end-effector position and orientation and ”
A New Actuation Approach for Human Friendly Robot Design (2004, methodology): “To address these limitations and create a unified high-performance and safe robotic manipulator a new actuation approach, referred to as the Distributed Macro-Mini actuation approach (DM2), has been proposed[15].”
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks (2019, methodology): “We then compute the robot actuations (joint torques) to achieve the desired end-effector space accelerations leveraging the kinematic and dynamic models of the robot with the dynamically-consistent operational space formulation [16].”
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks (2018, methodology): “By leveraging known kinematic and dynamics models of the robot, we can calculate joint torques from Cartesian space accelerations with the dynamically-consistent operational space formulation [30].”
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks (2019, methodology): “By leveraging known kinematic and dynamics models of the robot, we can calculate joint torques from Cartesian space accelerations with the dynamically consistent operational space formulation [79].”
A Unified Passivity-based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots (2007, methodology): “Based on the torque interface, well known methods used to deal with singularities and redundancy can be adapted from the rigid case, e.g., from Khatib (1995).”