Manipulation planning on constraint manifolds

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

We present an efficient approach to generating paths for a robotic manipulator that are collision-free and guaranteed to meet task specifications despite pose uncertainty. We first describe how to use Task Space Regions (TSRs) to specify grasping and object placement tasks for a manipulator. We then show how to modify a set of TSRs for a certain task to take into account pose uncertainty. A key advantage of this approach is that if the pose uncertainty is too great to accomplish a certain task, we can quickly reject that task without invoking a planner. If the task is not rejected we run the IKBiRRT planner, which trades-off exploring the robot's C-space with sampling from TSRs to compute a path. Finally, we show several examples of a 7-DOF WAM arm planning paths in a cluttered kitchen environment where the poses of all objects are uncertain.

ArXiv, 2021

Within the field of robotic manipulation, a central goal is to replicate the human ability to manipulate any object in any situation using a sequence of manipulation primitives such as grasping, pushing, inserting, sliding, etc. Conceptually, each manipulation primitive restricts the object and robot to move on a lower-dimensional manifold defined by the primitive’s dynamic equations of motion. Likewise, a manipulation sequence represents a dynamically feasible trajectory that traverses multiple manifolds. To manipulate any object in any situation, robotic systems must include the ability to automatically synthesize manipulation primitives (manifolds) and sequence those primitives into a coherent plan (find a path across the manifolds). This paper investigates a principled approach for solving dexterous manipulation planning. This approach is based on rapidly-exploring random trees which use contact modes to guide tree expansion along primitive manifolds [10]. This paper extends thi...

… International Journal of …, 2004

This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems. KEY WORDS-manipulation task planning, path planning, probabilistic roadmaps

2010 IEEE International Conference on Robotics and Automation, 2010

In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRTbased algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque. We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

International Conference on Robotics and Automation, 2007

This paper presents the ResolveSpatialConstraints (RSC) algorithm for manipulation planning in a domain with movable obstacles. Empirically we show that our algorithm quickly generates plans for simulated articulated robots in a highly nonlinear search space of exponential dimension. RSC is a reverse-time search that samples future robot actions and constrains the space of prior object displacements. To optimize the efficiency

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

In this paper, we present efficient solutions for planning motions of dual-arm manipulation and re-grasping tasks. Motion planning for such tasks on humanoid robots with a high number of degrees of freedom (DoF) requires computationally efficient approaches to determine the robot's full joint configuration at a given grasping position, i.e. solving the Inverse Kinematics (IK) problem for one or both hands of the robot. In this context, we investigate solving the inverse kinematics problem and motion planning for dual-arm manipulation and re-grasping tasks by combining a gradient-descent approach in the robot's pre-computed reachability space with random sampling of free parameters. This strategy provides feasible IK solutions at a low computation cost without resorting to iterative methods which could be trapped by joint-limits. We apply this strategy to dual-arm motion planning tasks in which the robot is holding an object with one hand in order to generate whole-body robot configurations suitable for grasping the object with both hands. In addition, we present two probabilistically complete RRT-based motion planning algorithms (J + -RRT and IK-RRT) that interleave the search for an IK solution with the search for a collision-free trajectory and the extension of these planners to solving re-grasping problems. The capabilities of combining IK methods and planners are shown both in simulation and on the humanoid robot ARMAR-III performing dual-arm tasks in a kitchen environment.

1995

Abstract An emerging paradigm in solving the classical motion planning problem (among static obstacles) is to capture the connectivity of the configuration space using a finite (but possibly large) set of landmarks (or nodes) in it. In this paper, the authors extend this paradigm to manipulation planning problem, where the goal is to plan the motion of a robot so that it can move a given object from an initial configuration to a final configuration while avoiding collisions with the static obstacles in the environment.

2011 IEEE International Conference on Robotics and Automation, 2011

Finding paths in high-dimensional spaces becomes difficult when we wish to optimize the cost of a path in addition to obeying feasibility constraints. Recently the T-RRT algorithm was presented as a method to plan in high-dimensional costspaces and it was shown to perform well across a variety of problems. However, since the T-RRT relies solely on sampling to explore the space, it has difficulty navigating cost-space chasmsnarrow low-cost regions surrounded by increasing cost. Such chasms are particularly common in planning for manipulators because many useful cost functions induce narrow or lowerdimensional low-cost areas. This paper presents the GradienT-RRT algorithm, which combines the T-RRT with a local gradient method to bias the search toward lower-cost regions. GradienT-RRT is effective at navigating chasms because it explores low-cost regions that are too narrow to explore by sampling alone. We compare the performance of T-RRT and GradienT-RRT on planning problems involving cost functions defined in workspace, task space, and C-space. We find that GradienT-RRT outperforms T-RRT in terms of the cost of the final path while maintaining better or comparable computation time. We also find that the cost of paths generated by GradienT-RRT is far less sensitive to changes in a key parameter, making it easier to tune the algorithm. Finally, we conclude with a demonstration of GradienT-RRT on a planning-withuncertainty task on the physical HERB robot.

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

This paper presents an algorithm for planning sequences of pushes, by which a robotic arm equipped with a single rigid finger can move a manipulated object (or manipulandum) towards a desired goal pose. Pushing is perhaps the most basic kind of manipulation, however it presents difficult challenges for planning, because of the complex relationship between manipulative pushing actions and resulting manipulandum motions. The motion planning literature has well developed paradigms for solving e.g. the piano-mover's problem, where the search occurs directly in the configuration space of the manipulandum object being moved. In contrast, in pushing manipulation, a plan must be built in the action space of the robot, which is only indirectly linked to the motion space of the manipulandum through a complex interaction for which inverse models may not be known.

2011

Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance.

Workshops at the Twenty-Sixth AAAI …, 2012

The most critical moves that determine the success of a manipulation task are performed within the close vicinities of the object prior to grasping, and the goal prior to the final placement. Memorizing these state-action sequences and reusing them can dramatically improve the task efficiency, whereas even the state-of-the-art planning algorithms may require significant amount of time and computational resources to generate a solution from scratch depending on the complexity and the constraints of the task. In this paper, we propose a hybrid approach that combines the reliability of the past experiences gained through demonstration and the flexibility of a generative motion planning algorithm, namely RRT * , to improve the task execution efficiency. As a side benefit of reusing these final moves, we can dramatically reduce the number of nodes used by the generative planner, hence the planning time, by either early-terminating the planner when the generated plan reaches a "recalled state", or deliberately biasing it towards the memorized state-action sequences that are feasible at the moment. This complementary combination of the already available partial plans and the generated ones yield to fast, reliable, and repeatable solutions.

… , Automation, Robotics and …, 2006

This paper addresses the motion planning problem of the dexterous manipulation of 3D rigid objects by a robotic multi-fingered hand. We propose a novel approach based on probabilistic roadmap techniques. Inspired by the theory developed in [1], [17], the planner relies on a topological property that characterizes the existence of solutions in GSn, a specific manifold of the configuration space. This property leads to reduce the problem by structuring the search-space. It allows us to design a manipulation planner that directly captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. The proposed method allows a global planning -both object and fingers trajectories are computed-that can cope with the obstacle presence in the environment. Collisions between different fingers or between object and fingers elsewhere than fingertips are avoided. Force closure constraints are taken into account to ensure the computed paths physical feasibility, under quasi-static motion assumption. First experiments demonstrate the feasibility and the efficiency of the approach.

1995

This paper deals with the manipulation planning problem, where the goal is to plan the motion of a robot so that it can move a given object from an initial con guration to a nal con guration while avoiding collisions with the static obstacles in the environment. Our speci c approach adapts Adraine's Clew Algorithm that has been shown effective for classical motion planning problem 1, 12]. In our approach, landmarks are placed in lower dimensional submanifolds of the composite con guration space. These landmarks represent stable grasps that are reachable from the initial con guration. From each new landmark, the planner attempts to reach the goal con guration by executing a local planner, again in a lower (but di erent) dimensional submanifold of the composite con guration space. We have implemented this approach and present initial experiments with a simple 2-dof planar arm among polygonal obstacles.

The International Journal of Robotics Research, 2011

Robots that perform complex manipulation tasks must be able to generate strategies that make and break contact with the object. This requires reasoning in a motion space with a particular multi-modal structure, in which the state contains both a discrete mode (the contact state) and a continuous configuration (the robot and object poses). In this paper we address multi-modal motion planning in the common setting where the state is high-dimensional, and there are a continuous infinity of modes. We present a highly general algorithm, Random-MMP, that repeatedly attempts mode switches sampled at random. A major theoretical result is that Random-MMP is formally reliable and scalable, and its running time depends on certain properties of the multi-modal structure of the problem that are not explicitly dependent on dimensionality. We apply the planner to a manipulation task on the Honda humanoid robot, where the robot is asked to push an object to a desired location on a cluttered table, ...

2011 IEEE International Conference on Robotics and Automation, 2011

In this paper, we present a search-based motion planning algorithm for manipulation that handles the high dimensionality of the problem and minimizes the limitations associated with employing a strict set of pre-defined actions. Our approach employs a set of adaptive motion primitives comprised of static motions with variable dimensionality and on-the-fly motions generated by two analytical solvers. This method results in a slimmer, multi-dimensional lattice and offers the ability to satisfy goal constraints with precision. To validate our approach, we used a 7DOF manipulator to perform experiments on a real mobile manipulation platform (Willow Garage's PR2). Our results demonstrate the effectiveness of the planner in efficiently navigating cluttered spaces; the method generates consistent, low-cost motion trajectories, and guarantees the search is complete with bounds on the suboptimality of the solution.