About this Research Topic
This Research Topic is based on outputs from the workshop 'Bridging the Gap Between Data-Driven and Analytical Physics-Based Grasping and Manipulation' at the International Conference on Robotics and Automation (ICRA) 2021. However, we would also welcome spontaneous submissions not associated with this workshop, which should fit the scope of the Research Topic.
Considerable progress in grasping and manipulation has been achieved using approaches that extract complex behaviors from data. Yet, data-driven approaches are mostly assessed empirically and not necessarily complying with physical and dynamical constraints compared to analytical approaches where these constraints can be modeled manually. Besides, the application of black-box learning models often results in limited success due to large data requirements, incompetence in yielding physically consistent results, and lack of generalizability to novel cases.
Meanwhile, physics-based approaches have also been improved in dealing with uncertainty. Yet, simplifying assumptions on, for example, contact and friction models and stationary environments are often needed, resulting in models that cannot account for variations arising when contact models are rich or environments are unstructured and dynamically change.
As neither a learning-based nor an analytic approach can be considered sufficient for complex manipulation tasks with high dimensional state spaces, a continuum between mechanistic and learning models is indispensable, where both domain-specific knowledge and data are integrated synergistically.
In contrast to practices based on simple forms of feature engineering, heuristics, and constraints, this Research Topic is focused on exploring a deeper coupling of learning-based methods with physics and discussing benefits of analytical and data-driven approaches in grasping and manipulation applications.
The aim of the current Research Topic is to cover promising and novel research trends in robotic grasping and manipulation, both from classical analytical/physics modeling-based and data-driven approaches. Areas covered in this Research Topic may include, but are not limited to:
● Learning and analytic approaches dealing with the uncertainty or unobservability in sensing and actuation during grasping and manipulation process
● Simulation to reality transfer
● Modeling, representation and integration of sensing modalities for grasp and manipulation tasks,e.g. proprioceptive, visual, force/torque, tactile, proximity sensing
● Grasping of known, partially known or novel objects
● New quality measures for grasping under uncertainty
● Learning-based approaches for grasp planning and manipulation: e.g. model-based, model-free, data-efficient, multi-task, transfer, meta-learning, reinforcement learning, learning from demonstration
● Analytic and hybrid approaches for grasping and manipulation
● Integration of data-driven with physics-based models for grasping and manipulation
● Modeling complex (object/hand/scene) interaction dynamics for grasp and manipulation tasks
● Integrating learning and control for grasping and manipulation
● Generalization and scalability of approaches to a variety of hands and objects
● Approaches addressing deformable/flexible object manipulation, dexterous grasping and manipulation, in-hand manipulation, bi-manual manipulation, mobile manipulation (e.g. legged, wheeled, aerial, underwater manipulation)
Keywords: Grasping and manipulation, Mobile manipulation, Robot learning, Motion planning, Perception and control
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.