Closed-Loop Object-Informed Control for Non-Prehensile Robot Manipulation
Overview
This research presents a closed-loop object-informed (CLOI) method for non-prehensile robot manipulation, addressing challenges in long-horizon planning with discontinuous contact. By splitting the problem into object-level and robot-level planning solved via model predictive path integral control and coupled through consensus using ADMM, CLOI achieves a 35% success rate increase in simulation and 43% on hardware for planar pushing tasks with obstacles compared to standard MPPI under the same computational budget.
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Environment: simulation
“s revised toward robot-realizable object trajectories, while the robot plan is aligned with the object poses the task requires. In planar pushing tasks with obstacles using an xArm6 manipulator, CLOI increases the success rate by 35% in simulation and 43% on hardware, compared to standard MPPI given the same computational budget.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 35 percent · Basis not reported · Differing source assertions
“s revised toward robot-realizable object trajectories, while the robot plan is aligned with the object poses the task requires. In planar pushing tasks with obstacles using an xArm6 manipulator, CLOI increases the success rate by 35% in simulation and 43% on hardware, compared to standard MPPI given the same computational budget.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 43 percent · Basis not reported · Differing source assertions
“s revised toward robot-realizable object trajectories, while the robot plan is aligned with the object poses the task requires. In planar pushing tasks with obstacles using an xArm6 manipulator, CLOI increases the success rate by 35% in simulation and 43% on hardware, compared to standard MPPI given the same computational budget.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalClosed-Loop Object-Informed Control for Non-Prehensile Robot Manipulation
This research presents a closed-loop object-informed (CLOI) method for non-prehensile robot manipulation, addressing challenges in long-horizon planning with discontinuous contact. By splitting the problem into object-level and robot-level planning solved via model predictive path integral control and coupled through consensus using ADMM, CLOI achieves a 35% success rate increase in simulation and 43% on hardware for planar pushing tasks with obstacles compared to standard MPPI under the same computational budget.
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