Mike Stilman

Task constrained motion planning in robot joint space

We explore global randomized joint space path planning for articulated robots that are subject to task space constraints. This paper describes a representation of constrained motion for joint space planners and develops two simple and efficient methods for constrained sampling of joint configurations: Tangent Space Sampling (TS) and First-Order Retraction (FR). Constrained joint space planning is important for many real world problems involving redundant manipulators. On the one hand, tasks are designated in work space coordinates: rotating doors about fixed axes, sliding drawers along fixed trajectories or holding objects level during transport. On the other, joint space planning gives alternative paths that use redundant degrees of freedom to avoid obstacles or satisfy additional goals while performing a task. In simulation, we demonstrate that our methods are faster and significantly more invariant to problem/algorithm parameters than existing techniques.

Navigation among movable obstacles: real-time reasoning in complex environments

In this paper, we address the problem of navigation among movable obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path. Simpler problems have been shown to be P-SPACE hard. For real-world scenarios with large numbers of movable obstacles, complete motion planning techniques are largely intractable. This paper presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multi-object domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.

Manipulation Planning Among Movable Obstacles

This paper presents the resolve spatial constraints (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 of RSC, we identify methods for sampling object surfaces and generating connecting paths between grasps and placements. In addition to experimental analysis of RSC, this paper looks into object placements and task-space motion constraints among other unique features of the three dimensional manipulation planning domain.

Global Manipulation Planning in Robot Joint Space With Task Constraints

We explore global randomized joint-space path planning for articulated robots that are subjected to task-space constraints. This paper describes a representation of constrained motion for joint-space planners and develops two simple and efficient methods for constrained sampling of joint configurations: tangent-space sampling (TS) and first-order retraction (FR). FR is formally proven to provide global sampling for linear task-space transformations. Constrained joint-space planning is important for many real-world problems, which involves redundant manipulators. On the one hand, tasks are designated in workspace coordinates: to rotate doors about fixed axes, to slide drawers along fixed trajectories, or to hold objects level during transport. On the other hand, joint-space planning gives alternative paths that use redundant degrees of freedom (DOFs) to avoid obstacles or satisfy additional goals while performing a task. We demonstrate that our methods are faster and more invariant to parameter choices than the techniques that exist.

Planning Among Movable Obstacles with Artificial Constraints

This paper presents artificial constraints as a method for guiding heuristic search in the computationally challenging domain of motion planning among movable obstacles. The robot is permitted to manipulate unspecified obstacles in order to create space for a path. A plan is an ordered sequence of paths for robot motion and object manipulation. We show that under monotone assumptions, anticipating future manipulation paths results in constraints on both the choice of objects and their placements at earlier stages in the plan. We present an algorithm that uses this observation to incrementally reduce the search space and quickly find solutions to previously unsolved classes of movable obstacle problems. Our planner is developed for arbitrary robot geometry and kinematics. It is presented with an implementation for the domain of navigation among movable obstacles.

Sampling heuristics for optimal motion planning in high dimensions

We present a sampling-based motion planner that improves the performance of the probabilistically optimal RRT* planning algorithm. Experiments demonstrate that our planner finds a fast initial path and decreases the cost of this path iteratively. We identify and address the limitations of RRT* in high-dimensional configuration spaces. We introduce a sampling bias to facilitate and accelerate cost decrease in these spaces and a simple node-rejection criteria to increase efficiency. Finally, we incorporate an existing bi-directional approach to search which decreases the time to find an initial path. We analyze our planner on a simple 2D navigation problem in detail to show its properties and test it on a difficult 7D manipulation problem to show its effectiveness. Our results consistently demonstrate improved performance over RRT*.

Planning and Executing Navigation Among Movable Obstacles

This paper explores autonomous locomotion, reaching, grasping and manipulation for the domain of navigation among movable obstacles (NAMO). The robot perceives and constructs a model of an environment filled with various fixed and movable obstacles, and automatically plans a navigation strategy to reach a desired goal location. The planned strategy consists of a sequence of walking and compliant manipulation operations. It is executed by the robot with online feedback. We give an overview of our NAMO system, as well as provide details of the autonomous planning, online grasping and compliant hand positioning during dynamically-stable walking. Finally, we present results of a successful implementation running on the humanoid robot HRP-2

Golem Krang: Dynamically stable humanoid robot for mobile manipulation

What would humans be like if nature had invented the wheel? Golem Krang is a novel humanoid torso designed at Georgia Tech. The robot dynamically transforms from a.5 m static to a 1.5 m dynamic configuration. Our robot development has led to two advances in the design of platforms for mobility and manipulation: (1) A 2-DOF robot base that autonomously stands from horizontal rest; (2) A 4-DOF humanoid torso that adds a waist roll joint to replicate human torso folding and a yaw joint for spine rotation. The mobile torso also achieves autonomous standing in a constrained space while lifting a 40 kg payload. Golem validates our assertions by consistently achieving static-dynamic transformations. This paper describes the design of our mobile torso. It considers a number of factors including its suitability for human environments, mechanical simplicity and the ability to store potential and kinetic energy for handling heavy human and even super-human tasks.

Push planning for object placement on cluttered table surfaces

We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation.

Path Planning among Movable Obstacles: A Probabilistically Complete Approach

In this paper we study the problem of path planning among movable obstacles, in which a robot is allowed to move the obstacles if they block the robot’s way from a start to a goal position. We make the observation that we can decouple the computations of the robot motions and the obstacle movements, and present a probabilistically complete algorithm, something which to date has not been achieved for this problem. Our algorithm maintains an explicit representation of the robot’s configuration space. We present an efficient implementation for the case of planar, axis-aligned environments and report experimental results on challenging scenarios.