A robot with contact sensing capability can reduce uncertainty relative to the environment by deliberately moving into contact and matching the resulting contact measurement to different possible states in the world. We present a manipulation planner that finds and sequences these actions by reasoning explicitly about the uncertainty over the robot's state. The planner incrementally constructs a policy that covers all possible contact states during a manipulation and finds contingencies for each of them. In contrast to conformant planners (without contingencies), the planned contingent policies are more robust. We demonstrate this in simulated and real-world manipulation experiments. In contrast to POMDP-based planners, we show that our planner can be directly applied to high-dimensional configuration spaces.

We describe the winning entry to the Amazon Picking Challenge 2015. From the experience of building this system and competing, we derive several conclusions: (1) We suggest to characterize robotic system building along four key aspects, each of them spanning a spectrum of solutions - modularity vs. integration, generality vs. assumptions, computation vs. embodiment, and planning vs. feedback. (2) To understand which region of each spectrum most adequately addresses which robotic problem, we must explore the full spectrum of possible approaches. (3) For manipulation problems in unstructured environments, certain regions of each spectrum match the problem most adequately, and should be exploited further. This is supported by the fact that our solution deviated from the majority of the other challenge entries along each of the spectra. This is an abridged version of a conference publication.

In this paper we present a planner that interleaves free-space motion with motion in contact to reduce uncertainty. The planner finds such motions by growing a search tree in the combined space of collision-free and contact configurations. The planner reasons efficiently about the accumulated uncertainty by factoring the state in a belief over configuration and a fully observable contact state. We show the uncertainty-reducing capabilities of the planner on manipulation benchmark from the POMDP literature. The planner scales up to more complex problems like manipulation under uncertainty in seven-dimensional configuration space. We validate our planner in simulation and on a real robot.

We present an incremental method for motion generation in environments with unpredictably moving and initially unknown obstacles. The key to the method is its incremental nature: it locally augments and adapts global motion plans in response to changes in the environment, even if they significantly change the connectivity of the world. The restriction to local changes to a global plan results from the fact that in mobile manipulation, robots can ultimately only rely on their on-board sensors to perceive changes in the world. The proposed method addresses three sub-problems of motion generation with three algorithmic components. The first component reactively adapts plans in response to small, continuous changes. The second augments the plan locally in response to connectivity changes. And the third extracts a global, goal-directed motion from the representation maintained by the first two components. In an experimental evaluation of this method, we show a real-world mobile manipulator executing a whole-body motion task in an initially unknown environment, while incrementally maintaining a plan using only on-board sensors.

Mobile manipulation targets applications in dynamic and unstructured environments. Motion generation methods suitable for these applications must account for end-effector task constraints, must reason about environment uncertainty, i.e. the fact that the exact state of the dynamic environment cannot be known to the robot, and should do so only using their on-board sensors. We present the Expected-Shortest-Path Elastic Roadmap (ESPER) planner as a motion generation method suitable for mobile manipulation. It integrates task-constrained, whole-body, reactive motion generation in high-dimensional configuration space with reasoning about uncertainty. In our experiments, we generate task-consistent motion in uncertain environments on a real-world mobile manipulator only relying on on-board sensors.

We present the exploring/exploiting tree (EET) algorithm for motion planning. The EET planner deliberately trades probabilistic completeness for computational efficiency. This tradeoff enables the EET planner to outperform state-of-the-art sampling-based planners by up to three orders of magnitude. We show that these considerable speedups apply for a variety of challenging real-world motion planning problems. The performance improvements are achieved by leveraging work space information to continuously adjust the sampling behavior of the planner. When the available information captures the planning problem's inherent structure, the planner's sampler becomes increasingly exploitative. When the available information is less accurate, the planner automatically compensates by increasing local configuration space exploration. We show that active balancing of exploration and exploitation based on workspace information can be a key ingredient to enabling highly efficient motion planning in practical scenarios.