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Humanoid Robotics Laboratory

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Probabilistic Human Action Prediction and Wait-sensitive Planning for Responsive Human-robot Collaboration

2013-10 , Hawkins, Kelsey P. , Vo, Nam , Bansal, Shray , Bobic, Aaron F.

A novel representation for the human component of multi-step, human-robot collaborative activity is presented. The goal of the system is to predict in a probabilistic manner when the human will perform different subtasks that may require robot assistance. The representation is a graphical model where the start and end of each subtask is explicitly represented as a probabilistic variable conditioned upon prior intervals. This formulation allows the inclusion of uncertain perceptual detections as evidence to drive the predictions. Next, given a cost function that describes the penalty for different wait times, we develop a planning algorithm which selects robot-actions that minimize the expected cost based upon the distribution over predicted human-action timings. We demonstrate the approach in assembly tasks where the robot must provide the right part at the right time depending upon the choices made by the human operator during the assembly.

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Planning with Movable Obstacles in Continuous Environments with Uncertain Dynamics

2013-05 , Levihn, Martin , Scholz, Jonathan , Stilman, Mike

In this paper we present a decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO) operating under conditions faced by real robotic systems. While planners for the NAMO domain exist, they typically assume a deterministic environment or rely on discretization of the configuration and action spaces, preventing their use in practice. In contrast, we propose a planner that operates in real-world conditions such as uncertainty about the parameters of workspace objects and continuous configuration and action (control) spaces. To achieve robust NAMO planning despite these conditions, we introduce a novel integration of Monte Carlo simulation with an abstract MDP construction. We present theoretical and empirical arguments for time complexity linear in the number of obstacles as well as a detailed implementation and examples from a dynamic simulation environment.

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Multi-Process Control Software for HUBO2 Plus Robot

2013-04 , Grey, M .X. , Dantam, Neil , Lofaro, Daniel M. , Bobick, Aaron F. , Egerstedt, Magnus B. , Oh, Paul , Stilman, Mike

Humanoid robots require greater software reliability than traditional mechantronic systems if they are to perform useful tasks in typical human-oriented environments. This paper covers a software architecture which distributes the load of computation and control tasks over multiple processes, enabling fail-safes within the software. These fail-safes ensure that unexpected crashes or latency do not produce damaging behavior in the robot. The distribution also offers benefits for future software development by making the architecture modular and extensible. Utilizing a low-latency inter-process communication protocol (Ach), processes are able to communicate with high control frequencies. The key motivation of this software architecture is to provide a practical framework for safe and reliable humanoid robot software development. The authors test and verify this framework on a HUBO2 Plus humanoid robot.

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Robust and Efficient Communication for Real-Time Multi-Process Robot Software

2012-11 , Dantam, Neil , Stilman, Mike

We present a new Interprocess Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating drivers, controllers, and algorithms in complex robotic systems such as humanoid robots. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems, and we discuss the implementation on our humanoid robot Golem Krang. Finally, the source code for Ach is available under an Open Source permissive license.

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The Motion Grammar: Analysis of a Linguistic Method for Robot Control

2013-06 , Dantam, Neil , Stilman, Mike

We present the Motion Grammar: an approach to represent and verify robot control policies based on Context-Free Grammars. The production rules of the grammar represent a top-down task decomposition of robot behavior. The terminal symbols of this language represent sensor readings that are parsed in real-time. Efficient algorithms for context-free parsing guarantee that online parsing is computationally tractable. We analyze verification properties and language constraints of this linguistic modeling approach, show a linguistic basis that unifies several existing methods, and demonstrate effectiveness through experiments on a 14-DOF manipulator interacting with 32 objects (chess pieces) and an unpredictable human adversary. We provide many of the algorithms discussed as Open Source, permissively licensed software. ¹

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Planning in Constraint Space: Automated Design of Functional Structures

2013-05 , Erdogan, Can , Stilman, Mike

On the path to full autonomy, robotic agents have to learn how to manipulate their environments for their benefit. In particular, the ability to design structures that are functional in overcoming challenges is imperative. The problem of automated design of functional structures (ADFS) addresses the question of whether the objects in the environment can be placed in a useful configuration. In this work, we first make the observation that the ADFS problem represents a class of problems in high dimensional, continuous spaces that can be broken down into simpler subproblems with semantically meaningful actions. Next, we propose a framework where discrete actions that induce constraints can partition the solution space effectively. Subsequently, we solve the original class of problems by searching over the available actions, where the evaluation criteria for the search is the feasibility test of the accumulated constraints. We prove that with a sound feasibility test, our algorithm is complete. Additionally, we argue that a convexity requirement on the constraints leads to significant efficiency gains. Finally, we present successful results to the ADFS problem.

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Detecting Partially Occluded Objects via Segmentation and Validation

2013-01 , Levihn, Martin , Dutton, Matthew , Trevor, Alexander J. B. , Stilman, Mike

This paper presents a novel algorithm: Verfied Partial Object Detector (VPOD) for accurate detection of partially occluded objects such as furniture in 3D point clouds. VPOD is implemented and validated on real sensor data obtained by our robot. It extends Viewpoint Feature His- tograms (VFH), which classify unoccluded objects, to also classify partially occluded objects such as furniture that might be seen in typical office environments. To achieve this result, VPOD employs two strategies. First, object models are segmented and the object database is extended to include partial models. Second, once a matching partial object is detected, the complete object model is aligned back into the scene and verified for consistency with the point cloud data. Overall, our approach increases the number of objects found and substantially reduces false positives due to the verification process.

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Path Planning with Uncertainty: Voronoi Uncertainty Fields

2013-05 , Ok, Kyel , Ansari, Sameer , Gallagher, Billy , Sica, William , Dellaert, Frank , Stilman, Mike

In this paper, a two-level path planning algorithm that deals with map uncertainty is proposed. The higher level planner uses modified generalized Voronoi diagrams to guarantee finding a connected path from the start to the goal if a collision-free path exists. The lower level planner considers uncertainty of the observed obstacles in the environment and assigns repulsive forces based on their distance to the robot and their positional uncertainty. The attractive forces from the Voronoi nodes and the repulsive forces from the uncertainty- biased potential fields form a hybrid planner we call Voronoi Uncertainty Fields (VUF). The proposed planner has two strong properties: (1) bias against uncertain obstacles, and (2) completeness. We analytically prove the properties and run simulations to validate our method in a forest-like environment.

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Humanoid Robot Teleoperation for Tasks with Power Tools

2013-04 , O’Flaherty, Rowland , Vieira, Peter , Grey, M. X. , Oh, Paul , Bobick, Aaron F. , Egerstedt, Magnus B. , Stilman, Mike

This paper presents the implementation of inverse kinematics to achieve teleoperation of a physical humanoid robot platform. The humanoid platform will be used to compete in the DARPA Robot Challenge, which requires autonomous execution of various search and rescue tasks, such as cutting through walls, which is a very practical application to robotics. Using a closed-form kinematic solution and a basic feedback controller, our objective of executing simple tasks is realized via teleoperation. Joint limits and singularities are accounted for using the different cases in the kinematic solution; and a decision method is implemented to determine how to position the end-effector when the goal is outside the feasible workspace.

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Deterministic Motion Planning for Redundant Robots along End-Effector Paths

2012-11 , Quispe, Ana Huamán , Stilman, Mike

In this paper we propose a deterministic approach to solve the Motion Planning along End-Effector Paths problem (MPEP) for redundant manipulators. Most of the existing approaches are based on local optimization techniques, hence they do not offer global guarantees of finding a path if it exists. Our proposed method is resolution complete. This feature is achieved by discretizing the Jacobian nullspace at each waypoint and selecting the next configuration according to a given heuristic function. To escape from possible local minima, our algorithm implements a backtracking strategy that allows our planner to recover from erroneous previous configuration choices by performing a breadth-first backwards search procedure. We present the results of simulated experiments performed with diverse manipulators and a humanoid robot.

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