Organizational Unit:

Humanoid Robotics Laboratory

Permanent Link

https://hdl.handle.net/1853/70718

Parent Organization

Organizational Unit

Institute for Robotics and Intelligent Machines (IRIM)

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Publication Search Results

Now showing 1 - 10 of 52

Krang: Center of Mass Estimation

(Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Volle, Kyle ; Stilman, Mike
Towards Stable Balancing

(Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Stilman, Mike
Autonomous Environment Manipulation to Assist Humanoid Locomotion

(Georgia Institute of Technology, 2014) Levihn, Martin ; Nishiwaki, Koichi ; Kagami, Satoshi ; Stilman, Mike

Legged robots have unique capabilities to traverse complex environments by stepping over and onto objects. Many footstep planners have been developed to take advantage of these capabilities. However, legged robots also have inherent constraints such as a maximum step height and distance. These constraints typically limit their reachable space, independent of footstep planning. Thus, we propose that robots such as humanoid robots that have manipulation capabilities should use them. A robot should autonomously modify its environment if necessary. We present a system that enabled a real robot to use a box to create itself a stair step or place a board on the ground to cross a gap, allowing it to reach its otherwise unreachable goal configuration.
Krang Kinematics: A Denavit-Hartenberg Parameterization

(Georgia Institute of Technology, 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike
Gravity and Drift in Force/Torque Measurements

( 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike
Foresight and Reconsideration in Hierarchical Planning and Execution

(Georgia Institute of Technology, 2013-11) Levihn, Martin ; Kaelbling, Leslie Pack ; Lozano-Pérez, Tomás ; Stilman, Mike

We present a hierarchical planning and execution architecture that maintains the computational efficiency of hierar- chical decomposition while improving optimality. It provides mech- anisms for monitoring the belief state during execution and per- forming selective replanning to repair poor choices and take advan- tage of new opportunities. It also provides mechanisms for looking ahead into future plans to avoid making short-sighted choices. The effectiveness of this architecture is shown through comparative experiments in simulation and demonstrated on a real PR2 robot.
Probabilistic Human Action Prediction and Wait-sensitive Planning for Responsive Human-robot Collaboration

(Georgia Institute of Technology, 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.
The Motion Grammar: Analysis of a Linguistic Method for Robot Control

(Georgia Institute of Technology, 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
Correct Software Synthesis for Stable Speed-Controlled Robotic Walking

(Georgia Institute of Technology, 2013-06) Dantam, Neil ; Hereid, Ayonga ; Ames, Aaron ; Stilman, Mike

We present a software synthesis method for speed- controlled robot walking based on supervisory control of a context-free Motion Grammar. First, we use Human-Inspired control to identify parameters for fixed speed walking and for transitions between fixed speeds, guaranteeing dynamic stability. Next, we build a Motion Grammar representing the discrete- time control for this set of speeds. Then, we synthesize C code from this grammar and generate supervisors¹ online to achieve desired walking speeds, guaranteeing correctness of discrete computation. Finally, we demonstrate this approach on the Aldebaran NAO, showing stable walking transitions with dynamically selected speeds.
Planning with Movable Obstacles in Continuous Environments with Uncertain Dynamics

(Georgia Institute of Technology, 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.