Promoting a robot agent’s autonomy level, which allows it to understand the human operator’s intent and provide motion assistance to achieve it, has demonstrated great advantages to the operator’s intent in teleoperation. However, the research has been limited to the target approaching process. We advance the shared control technique one step further to deal with the more challenging object manipulation task. Appropriately manipulating an object is challenging as it requires fine motion constraints for a certain manipulation task. Although these motion constraints are critical for task success, they are subtle to observe from ambiguous human motion. The disembodiment problem and physical discrepancy between the human and robot hands bring additional uncertainty, make the object manipulation task more challenging. Moreover, there is a lack of modeling and planning techniques that can effectively combine the human motion input and robot agent’s motion input while accounting for the ambiguity of the human intent. To overcome this challenge, we built a multi-task robot grasping model and developed an intent-uncertainty-aware grasp planner to generate robust grasp poses given the ambiguous human intent inference inputs. With this validated modeling and planning techniques, it is expected to extend teleoperated robots’ functionality and adoption in practical telemanipulation scenarios.
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Intent-Uncertainty-Aware Grasp Planning for Robust Robot Assistance in Telemanipulation
Semantic Scholar · Engineering · 2019
Abstract
Promoting a robot agent’s autonomy level, which allows it to understand the human operator’s intent and provide motion assistance to achieve it, has demonstrated great advantages to the operator’s intent in teleoperation. However, the research has been limited to the target approaching process. We advance the shared control technique one step further to deal with the more challenging object manipulation task. Appropriately manipulating an object is challenging as it requires fine motion constraints for a certain manipulation task. Although these motion constraints are critical for task success, they are subtle to observe from ambiguous human motion. The disembodiment problem and physical discrepancy between the human and robot hands bring additional uncertainty, make the object manipulation task more challenging. Moreover, there is a lack of modeling and planning techniques that can effectively combine the human motion input and robot agent’s motion input while accounting for the ambiguity of the human intent. To overcome this challenge, we built a multi-task robot grasping model and developed an intent-uncertainty-aware grasp planner to generate robust grasp poses given the ambiguous human intent inference inputs. With this validated modeling and planning techniques, it is expected to extend teleoperated robots’ functionality and adoption in practical telemanipulation scenarios.