Learning from demonstration (LfD) is a common technique applied to many problems in robotics, such as populating grasp databases, training for reinforcement learning of high-level skill sets and bootstrapping motion planners. While such approaches are generally highly valued, they rely on the often time-consuming process of gathering user demonstrations, and hence it becomes difficult to attain a sizeable dataset. In this paper, we present a tool capable of recording large numbers of high-dimensional demonstrations of mobile manipulation tasks provided by non-experts in the field. Our tool accomplishes this via a web interface that requires no additional software to be installed beyond a web browser, as well as a scalable architecture that is capable of supporting 10 concurrent demonstrators on a single server. Our architecture employs a lightweight simulation environment to reduce unnecessary computations and improve performance. Furthermore, we show how our tool can be used to gather a large set of demonstrations of a mobile manipulation task by leveraging existing crowdsource platforms. The data set collected has been made available to the robotics community. We also present experiments in which we apply demonstrations collected through our infrastructure to teach a robot how to grasp, to teach a robot how to perform dexterous manipulation tasks such as scooping and to accelerate motion planning for full-body manipulation tasks.

Many robotic systems are comprised of two or more arms. Such systems range from dual-arm household manipulators to factory floors populated with a multitude of industrial robotic arms. While the use of multiple arms increases the productivity of the system and extends dramatically its workspace, it also introduces a number of challenges. One such challenge is planning the motion of the arm(s) required to relocate an object from one location to another. This problem is challenging because it requires reasoning over which arms and in which order should manipulate the object, finding a sequence of valid handoff locations between the consecutive arms and finally choosing the grasps that allow for successful handoffs. In this paper, we show how to exploit the characteristics of this problem in order to construct a planner that can solve it effectively. We analyze our approach experimentally on a number of simulated examples ranging from a 2-arm system operating at a table to a 3-arm system working at a bar and to a 4-arm system in a factory setting.

Advanced modern humanoid robots often have complex manipulators with a large number of degrees of freedom. Thus, motion planning for such manipulators is a very computationally challenging problem. However, often robotic manipulators allow the wrist degrees of freedom to be controlled independently from the configuration of the rest of the arm. In this paper we show how to split the high dimensional planning problem into two lower-dimensional sub-problems - planning for the main arm joints and planning for the wrist joints, without losing guarantees on completeness. This approach is an extension of our previously developed framework for planning with adaptive dimensionality. Experimentally, we show that this approach is very effective in speeding up planning for robotic arms on Willow Garage's PR2 platform. We compare our algorithm with several popular alternative approaches for performing motion planning for robotic arms. The results we observe illustrate that our algorithm provides a good balance between planning time, planning success rate, path consistency, and path quality.

Kinematic planning for robotic arms has been shown to be capable of planning for robust manipulation of static objects. However, this approach falls short when manipulating moving objects such as picking up a jar off of a conveyor belt at a bottling plant. The challenge in carefully picking up moving objects is that these actions require motions that do not involve large decelerations, to avoid jerking the object, as well as figuring out the proper time in which the object can be picked up. We present a search-based kinodynamic motion planning algorithm that generates a time-parameterized trajectory for both the arm and end-effector, capable of carefully picking up the object at the earliest feasible point in its trajectory. To combat the high-dimensionality of the time-parameterized kinodynamic planning problem, our approach employs informative heuristics and adaptive dynamic motion primitives. To validate our approach, we used a 7DOF manipulator on Willow Garage's PR2 robot to pickup objects off of a conveyor belt. We also provide a detailed set of results that demonstrate the planner's ability to generate consistent, low cost trajectories for manipulation.

Mobile manipulators have brought a new level of flexibility to traditional automation tasks such as tabletop manipulation, but are not yet capable of the same speed and reliability as industrial automation. We present approaches to 3D perception and manipulator motion planning that enable a general purpose robotic platform to recognize and manipulate a variety of objects at a rate of one pick-and-place operation every 6.7 s, and work with a conveyor belt carrying objects at a speed of 33cm/s.

Dual-arm manipulation is an increasingly important skill for robots operating in home, retail and industrial environments. Dual-arm manipulation is especially essential for tasks involving large objects which are harder to grasp and manipulate using a single arm. In this work, we address dual-arm manipulation of objects in indoor environments. We are particularly focused on tasks that involve an upright orientation constraint on the grasped object. Such constraints are often present in human environments, e.g. when manipulating a tray of food or a container with fluids. In this paper, we present a search-based approach that is capable of planning dual-arm motions, often within one second, in cluttered environments while adhering to the orientation constraints. Our approach systematically constructs a graph in task space and generates motions that are consistent across runs with similar start/goal configurations and are low-cost. These motions come with guarantees on completeness and bounds on the suboptimality with respect to the graph that encodes the planning problem. For many problems, the consistency of the generated motions is important as it helps make the actions of the robot more predictable for a human interacting with the robot.

Randomized planners, search-based planners, potential-field approaches and trajectory optimization based motion planners are just some of the types of approaches that have been developed for motion planning. Given a motion planning problem, choosing the appropriate algorithm to use is a daunting task even for experts since there has been relatively little effort in comparing the plans generated by the different approaches, for different problems. In this paper, we present a set of benchmarks and the associated infrastructure for comparing different types of motion planning approaches and algorithms. The benchmarks are specifically designed for robotics and include typical indoor human environments. We present example motion planning problems for single arm tasks. Our infrastructure is designed to be easily extensible to allow for the addition of new planning approaches, new robots, new environments and new metrics. We present results comparing the performance of several motion planning algorithms to validate the use of these benchmarks.

In this paper, we develop an online motion planning approach which learns from its planning episodes (experiences) a graph, an Experience Graph. On the theoretical side, we show that planning with Experience graphs is complete and provides bounds on suboptimality with respect to the graph that represents the original planning problem. Experimentally, we show in simulations and on a physical robot that our approach is particularly suitable for higher-dimensional motion planning tasks such as planning for two armed mobile manipulation.

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.

Path planning quickly becomes computationally hard as the dimensionality of the state-space increases. In this paper, we present a planning algorithm intended to speed up path planning for high-dimensional state-spaces such as robotic arms. The idea behind this work is that while planning in a high-dimensional state-space is often necessary to ensure the feasibilityof the resulting path, large portions of the path have a lower-dimensional structure. Based on this observation, our algorithm iteratively constructs a state-space of an adaptive dimensionality--a state-space that is high-dimensional only where the higher dimensionality is absolutely necessary for finding a feasible path. This often reduces drastically the size of the state-space, and as a result, the planning time and memory requirements. Analytically, we show that our method is complete and is guaranteed to find a solution if one exists, within a specified suboptimality bound. Experimentally, we apply the approach to 3D vehicle navigation (x, y, heading), and to a 7 DOF robotic arm on the Willow Garage’s PR2 robot. The results from our experiments suggest that ourmethod can be substantially faster than some of the state-of-the-art planning algorithms optimized for those tasks.

Computing a motion that enables a mobile manipulator to open a door is challenging because it requires tight coordination between the motions of the arm and the base. Hard-coding the motion, on the other hand, is infeasible since doors vary widely in their sizes and types, some doors are opened by pulling and others by pushing, and indoor spaces often contain obstacles that limit the freedom of the mobile manipulator and the degree to which the doors open up. In this paper, we show how to overcome the high-dimensionality of the planning problem by identifying a graph-based representation that is small enough for efficient planning yet rich enough to contain feasible motions that open doors. The use of graph search-based motion planning enables us to handle consistently the wide variance of conditions under which doors need to be open. We demonstrate our approach on the PR2 robot - a mobile manipulator with an omnidirectional base and a 7 degree of freedom arm. The robot was successful in opening a variety of doors both by pulling and pushing.

Heuristic searches such as A* search are highly popular means of finding least-cost plans due to their generality, strong theoretical guarantees on completeness and optimality and simplicity in the implementation. In planning for robotic manipulation however, these techniques are commonly thought of as impractical due to the high-dimensionality of the planning problem. In this paper, we present a heuristic search-based manipulation planner that does deal effectively with the high-dimensionality of the problem. The planner achieves the required efficiency due to the following three factors: (a) its use of informative yet fast-to-compute heuristics; (b) its use of basic (small) motion primitives as atomic actions; and (c) its use of ARA* search which is an anytime heuristic search with provable bounds on solution suboptimality. Our experimental analysis on a real mobile manipulation platform with a 7-DOF robotic manipulator shows the ability of the planner to solve manipulation in cluttered spaces by generating consistent, low-cost motion trajectories while providing guarantees on completeness and bounds on suboptimality.

Visual time to collision estimation for small or micro air vehicles is challenging due to aggressive 6-DOF motion, real time performance requirements and significant size, weight and power constraints of the platform. Recent work in collision detection using insect inspired optical flow based methods have been demonstrated in low power hardware implementations [1][2][3][4], but have not achieved the obstacle detection and false alarm rate performance necessary for practical deployment. This performance is sensitive to correspondence errors in the optical flow field, so one approach to improving performance is to use a richer feature set for correspondence, along with calibrated inertial information from the platform to aid correspondence. In this video, we show proof of concept results for such an approach. Estimation results are noisy, but encouraging, and given that SIFT feature correspondence has been demonstrated in real time on low power GPUs, it has the potential for future small UAV integration.