4,012 research outputs found
A survey of robot manipulation in contact
In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks
Deep Model Predictive Variable Impedance Control
The capability to adapt compliance by varying muscle stiffness is crucial for
dexterous manipulation skills in humans. Incorporating compliance in robot
motor control is crucial to performing real-world force interaction tasks with
human-level dexterity. This work presents a Deep Model Predictive Variable
Impedance Controller for compliant robotic manipulation which combines Variable
Impedance Control with Model Predictive Control (MPC). A generalized Cartesian
impedance model of a robot manipulator is learned using an exploration strategy
maximizing the information gain. This model is used within an MPC framework to
adapt the impedance parameters of a low-level variable impedance controller to
achieve the desired compliance behavior for different manipulation tasks
without any retraining or finetuning. The deep Model Predictive Variable
Impedance Control approach is evaluated using a Franka Emika Panda robotic
manipulator operating on different manipulation tasks in simulations and real
experiments. The proposed approach was compared with model-free and model-based
reinforcement approaches in variable impedance control for transferability
between tasks and performance.Comment: Preprint submitted to the journal of robotics and autonomous system
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