We propose a model predictive control approach to pushing based manipulation. The key feature of the algorithm is that it can adapt to the accuracy requirements of a task, by slowing down and generating "careful" motion when the task requires high accuracy, and by speeding up and moving fast when the task allows inaccuracy. We formulate the problem as an MDP and use an approximate online solution to the MDP. We use a trajectory optimizer with a deterministic model to suggest promising actions to the MDP, to reduce computation time spent on evaluating different actions. The trajectory optimizer is then initialized with trajectories with different speed profiles to generate a variety of actions for the MDP that can adapt to different tasks.
Robots are becoming increasingly capable of performing complex manipulation tasks @cite_18 by leveraging the environment's physics @cite_15 . A traditional approach to complete manipulation tasks is motion planning @cite_11 @cite_0 , followed by open-loop execution. However, these plans are likely to fail due to uncertainty. Others @cite_12 @cite_16 @cite_8 @cite_17 have proposed using conservative open-loop motion plans that are robust to uncertainty. Recently, MPC algorithms for manipulation @cite_3 @cite_2 @cite_6 @cite_9 have been developed. They use feedback and can handle bounded uncertainty @cite_10 . We take a similar closed-loop approach to non-prehensile manipulation in this paper.
@cite_18 @cite_8 @cite_9 @cite_17 @cite_3 @cite_6 @cite_0 @cite_2 @cite_15 @cite_16 @cite_10 @cite_12 @cite_11
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Robotic manipulation involves actions where contacts occur between the robot and the objects. In this scope, the availability of physics-based engines allows motion planners to comprise dynamics between rigid bodies, which is necessary for planning this type of actions. However, physics-based motion planning is computationally intensive due to the high dimensionality of the state space and the need to work with a low integration step to find accurate solutions. On the other hand, manipulation actions change the environment and conditions further actions and motions. To cope with this issue, the representation of manipulation actions using ontologies enables a semantic-based inference process that alleviates the computational cost of motion planning. This paper proposes a manipulation planning framework where physics-based motion planning is enhanced with ontological knowledge representation and reasoning. The proposal has been implemented and is illustrated and validated with a simple example. Its use in grasping tasks in cluttered environments is currently under development. Manipulation planning involves planning the combined motion of objects in the environment as well as the robot motions to achieve them. In this paper, we explore a hierarchical approach to planning sequences of non-prehensile and prehensile actions. We subdivide the planning problem into three stages (object contacts, object poses and robot contacts) and thereby reduce the size of search space that is explored. We show that this approach is more efficient than earlier strategies that search in the combined robot-object configuration space directly. To cope with the growing complexity of manipulation tasks, the way to combine and access information from high- and low-planning levels has recently emerged as an interesting challenge in robotics. To tackle this, the present paper first represents the manipulation problem, involving knowledge about the world and the planning phase, in the form of an ontology. It also addresses a high-level and a low-level reasoning processes, this latter related with physics-based issues, aiming to appraise manipulation actions and prune the task planning phase from dispensable actions. In addition, a procedure is contributed to run these two-level reasoning processes simultaneously in order to make task planning more efficient. Eventually, the proposed planning approach is implemented and simulated through an example. Planning robust robot manipulation requires good forward models that enable robust plans to be found. This work shows how to achieve this using a forward model learned from robot data to plan push manipulations. We explore learning methods (Gaussian Process Regression, and an Ensemble of Mixture Density Networks) that give estimates of the uncertainty in their predictions. These learned models are utilised by a model predictive path integral (MPPI) controller to plan how to push the box to a goal location. The planner avoids regions of high predictive uncertainty in the forward model. This includes both inherent uncertainty in dynamics, and meta uncertainty due to limited data. Thus, pushing tasks are completed in a robust fashion with respect to estimated uncertainty in the forward model and without the need of differentiable cost functions. We demonstrate the method on a real robot, and show that learning can outperform physics simulation. Using simulation, we also show the ability to plan uncertainty averse paths.