@misc {pub5559,
	title = {Hybrid planning and control for changing contact manipulation},
	author = {Yanrong Wang  AND Mohan Sridharan AND Dirk Ruiken},
	year = {2024},
	month = {January},
	abstract = {Many existing architectures for robots that move and interact with their domain include an approach for Task and Motion planning (TAMP). This approach combines high-level, abstract task planning, often with a discrete, symbolic representation, and low-level motion planning and control based on a continuous representation. Despite considerable research, there are some key open problems in TAMP. First, the differences in representation and processing mechanisms between discrete-space task planning and continuous-space motion planning make it challenging to achieve an effective bidirectional flow of information between the two components. Second, many robot (and human) manipulation tasks are changing-contact tasks, which require the robot to make and break contact with objects and surfaces. The associated discontinuous interaction dynamics can damage the robot and the domain objects, but existing TAMP methods often disregard this kind of dynamics or do not address the discontinuities. Third, existing TAMP methods do not fully support the run-time adaptation of models at the level of task planning, motion planning, and control, which is necessary to account for uncertainties and unexpected changes that occur in complex domains. We describe an architecture that draws inspiration from insights into human motor control and research in cognitive systems, incorporating the principle of iterative refinement, heuristic methods, and incrementally-learned predictive (forward) models toward addressing these open problems. The architecture supports representational unification and process unification, and key features of the architecture are outlined below.},
	publisher = {UK Manipulation Workshop},
	booktitle = {The 5th UK Robot Manipulation Workshop}
}