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Recent Articles in Robotics
Physics-Based Grasp Planning Through Clutter, Mehmet R. Dogar, Kaijen Hsaio, Matei Ciocarlie, Siddhartha Srinivasa 
Carnegie Mellon University
Physics-Based Grasp Planning Through Clutter, Mehmet R. Dogar, Kaijen Hsaio, Matei Ciocarlie, Siddhartha Srinivasa
Robotics Institute
We propose a planning method for grasping in cluttered environments, a method where the robot can make simultaneous contact with multiple objects. With this method, the robot reaches for and grasps the target while simultaneously contacting and moving aside objects to clear a desired path. We use a physics-based analysis of pushing to compute the motion of each object in the scene in response to a set of possible robot motions. Our method enables multiple robot-object interactions, interactions that can be pre-computed and cached. However, our method avoids object-object interactions to make the problem computationally tractable. Through tests on large ...
Learning The Communication Of Intent Prior To Physical Collaboration, Kyle Strabala, Min Kyung Lee, Anca Dragan, Jodi Forlizzi, Siddhartha Srinivasa Carnegie Mellon University
Learning The Communication Of Intent Prior To Physical Collaboration, Kyle Strabala, Min Kyung Lee, Anca Dragan, Jodi Forlizzi, Siddhartha Srinivasa
Robotics Institute
When performing physical collaboration tasks, like packing a picnic basket together, humans communicate strongly and often subtly via multiple channels like gaze, speech, gestures, movement and posture. Understanding and participating in this communication enables us to predict a physical action rather than react to it, producing seamless collaboration. In this paper, we automatically learn key discriminative features that predict the intent to handover an object using machine learning techniques. We train and test our algorithm on multi-channel vision and pose data collected from an extensive user study in an instrumented kitchen. Our algorithm outputs a tree of possibilities, automatically encoding ...
Online Customization Of Teleoperation Interfaces, Anca Dragan, Siddhartha Srinivasa Carnegie Mellon University
Online Customization Of Teleoperation Interfaces, Anca Dragan, Siddhartha Srinivasa
Robotics Institute
In teleoperation, the user's input is mapped onto the robot via a motion retargetting function. This function must differ between robots because of their different kinematics, between users because of their different preferences, and even between tasks that the users perform with the robot. Our work enables users to customize this retargetting function, and achieve any of these required differences. In our approach, the robot starts with an initial function. As the user teleoperates the robot, he can pause and provide example correspondences, which instantly update the retargetting function. We select the algorithm underlying these updates by formulating the ...
Formalizing Assistive Teleoperation, Anca Dragan, Siddhartha Srinivasa Carnegie Mellon University
Formalizing Assistive Teleoperation, Anca Dragan, Siddhartha Srinivasa
Robotics Institute
In assistive teleoperation, the robot helps the user accomplish the desired task, making teleoperation easier and more seamless. Rather than simply executing the user's input, which is hindered by the inadequacies of the interface, the robot attempts to predict the user's intent, and assists in accomplishing it. In this work, we are interested in the scientific underpinnings of assistance: we formalize assistance under the general framework of policy blending, show how previous work methods instantiate this formalism, and provide a principled analysis of its main components: prediction of user intent and its arbitration with the user input. We ...
Herb 2.0: Lessons Learned From Developing A Mobile Manipulator For The Home, Siddhartha Srinivasa, Dmitry Berenson, Maya Cakmak, Alvaro Collet Romea, Mehmet R. Dogar, Anca Dragan, Ross A. Knepper, Tim Niemueller, Kyle Strabala, Mike Vande Weghe, Julius Ziegler Carnegie Mellon University
Herb 2.0: Lessons Learned From Developing A Mobile Manipulator For The Home, Siddhartha Srinivasa, Dmitry Berenson, Maya Cakmak, Alvaro Collet Romea, Mehmet R. Dogar, Anca Dragan, Ross A. Knepper, Tim Niemueller, Kyle Strabala, Mike Vande Weghe, Julius Ziegler
Robotics Institute
We present the hardware design, software architecture, and core algorithms of HERB 2.0, a bimanual mobile manipulator developed at the Personal Robotics Lab at Carnegie Mellon University. We have developed HERB 2.0 to perform useful tasks for and with people in human environments. We exploit two key paradigms in human environments: that they have structure that a robot can learn, adapt and exploit, and that they demand general-purpose capability in robotic systems. In this paper, we reveal some of the structure present in everyday environments that we have been able to harness for manipulation and interaction, comment on ...
Generating Legible Motion, Anca Dragan, Siddhartha Srinivasa Carnegie Mellon University
Generating Legible Motion, Anca Dragan, Siddhartha Srinivasa
Robotics Institute
Legible motion --- motion that communicates its intent to a human observer --- is crucial for enabling seamless human-robot collaboration. In this paper, we propose a functional gradient optimization technique for autonomously generating legible motion. Our algorithm optimizes a legibility metric inspired by the psychology of action interpretation in humans, resulting in motion trajectories that purposefully deviate from what an observer would expect in order to better convey intent. A trust region constraint on the optimization ensures that the motion does not become too surprising or unpredictable to the observer. Our studies with novice users that evaluate the resulting trajectories support the ...
Chomp: Covariant Hamiltonian Optimization For Motion Planning, Matt Zucker, Nathan Ratliff, Anca Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M. Dellin, J. Andrew Bagnell, Siddhartha Srinivasa Carnegie Mellon University
Chomp: Covariant Hamiltonian Optimization For Motion Planning, Matt Zucker, Nathan Ratliff, Anca Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M. Dellin, J. Andrew Bagnell, Siddhartha Srinivasa
Robotics Institute
In this paper, we present CHOMP (Covariant Hamiltonian Optimization for Motion Planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to low- cost trajectories even when initialized with infeasible ones. It uses Hamiltonian Monte Carlo to alleviate the problem of convergence to high-cost local minima (and for probabilistic completeness), and is capable of respecting ...
Efficient Touch Based Localization Through Submodularity, Shervin Javdani, Matthew Klingensmith, J. Andrew Bagnell, Nancy Pollard, Siddhartha Srinivasa Carnegie Mellon University
Efficient Touch Based Localization Through Submodularity, Shervin Javdani, Matthew Klingensmith, J. Andrew Bagnell, Nancy Pollard, Siddhartha Srinivasa
Robotics Institute
Many robotic systems deal with uncertainty by performing a sequence of information gathering actions. In this work, we focus on the problem of efficiently constructing such a sequence by drawing an explicit connection to submodularity. Ideally, we would like a method that finds the optimal sequence of actions, taking the minimum amount of time while providing sufficient information. Finding this sequence, however, is generally intractable. As a result, many well-established methods select actions greedily. Surprisingly, this often performs well even with only one step lookahead. Our work first explains this high performance -- we note that a commonly used metric, reduction ...
Exploiting Domain Knowledge For Object Discovery, Alvaro Collet Romea, Bo Xiong, Corina Gurau, Martial Hebert, Siddhartha Srinivasa Carnegie Mellon University
Exploiting Domain Knowledge For Object Discovery, Alvaro Collet Romea, Bo Xiong, Corina Gurau, Martial Hebert, Siddhartha Srinivasa
Robotics Institute
In this paper, we consider the problem of Lifelong Robotic Object Discovery (LROD) as the long-term goal of discovering novel objects in the environment while the robot operates, for as long as the robot operates. As a first step towards LROD, we automatically process the raw video stream of an entire workday of a robotic agent to discover objects. We claim that the key to achieve this goal is to incorporate domain knowledge whenever available, in order to detect and adapt to changes in the environment. We propose a general graph-based formulation for LROD in which generic domain knowledge is ...
Object Search By Manipulation, Mehmet R. Dogar, Michael C. Koval, Abhijeet Tallavajhula, Siddhartha Srinivasa Carnegie Mellon University
Object Search By Manipulation, Mehmet R. Dogar, Michael C. Koval, Abhijeet Tallavajhula, Siddhartha Srinivasa
Robotics Institute
We investigate the problem of a robot searching for an object. This requires reasoning about both perception and manipulation: certain objects are moved because the target may be hidden behind them and others are moved because they block the manipulator's access to other objects. We contribute a formulation of the object search by manipulation problem using visibility and accessibility relations between objects. We also propose a greedy algorithm and show that it is optimal under certain conditions. We propose a second algorithm which is optimal under all conditions. This algorithm takes advantage of the structure of the visibility and ...
Pose Estimation For Contact Manipulation With Manifold Particle Filters, Michael C. Koval, Mehmet R. Dogar, Nancy Pollard, Siddhartha Srinivasa Carnegie Mellon University
Pose Estimation For Contact Manipulation With Manifold Particle Filters, Michael C. Koval, Mehmet R. Dogar, Nancy Pollard, Siddhartha Srinivasa
Robotics Institute
We investigate the problem of estimating the state of an object during manipulation. Contact sensors provide valuable information about the object state during actions which involve persistent contact, e.g. pushing. However, contact sensing is very discriminative by nature, and therefore the set of object states which contact a sensor constitutes a lower-dimensional manifold in the state space of the object. This causes stochastic state estimation methods such as particle filters to perform poorly when contact sensors are used. We propose a new algorithm, the manifold particle filter, which uses dual particles directly sampled from the contact manifold to avoid ...
Effects Of Robot Capability On User Acceptance, Elizabeth Cha, Anca Dragan, Siddhartha Srinivasa Carnegie Mellon University
Effects Of Robot Capability On User Acceptance, Elizabeth Cha, Anca Dragan, Siddhartha Srinivasa
Robotics Institute
Although personal robots hold great promise, they face many barriers to being accepted and adopted in our homes [1]–[3]. As these robots are expected to perform personal tasks, their burden of trustworthiness is far greater than that of service or industrial robots. Several studies have explored our acceptance of robots, studying factors such as natural interaction via speech [4], gaze [5], and appearance [4], expectation setting via apologizing [6], and predictability of behavior [1].
Towards Seamless Human-Robot Handovers, Kyle Strabala, Min Kyung Lee, Anca Dragan, Jodi Forlizzi, Siddhartha Srinivasa, Maya Cakmak, Vincenzo Micelli Carnegie Mellon University
Towards Seamless Human-Robot Handovers, Kyle Strabala, Min Kyung Lee, Anca Dragan, Jodi Forlizzi, Siddhartha Srinivasa, Maya Cakmak, Vincenzo Micelli
Robotics Institute
A handover is a complex collaboration, where actors coordinate in time and space to transfer control of an object. This coordination comprises two processes: the physical process of moving to get close enough to transfer the object, and the cognitive process of exchanging information to guide the transfer. Despite this complexity, we humans are capable of performing handovers seamlessly in a wide variety of situations, even when unexpected. This suggests a common procedure that guides all handover interactions. Our goal is to codify that procedure. To that end, we first study how people hand objects to each other in order ...
Teleoperation With Intelligent And Customizable Interfaces, Anca Dragan, Kenton C.T. Lee, Siddhartha Srinivasa Carnegie Mellon University
Teleoperation With Intelligent And Customizable Interfaces, Anca Dragan, Kenton C.T. Lee, Siddhartha Srinivasa
Robotics Institute
In this paper, we explore a class of teleoperation problems where a user controls a sophisticated device (e.g. a robot) via an interface to perform a complex task. Teleoperation interfaces are fundamentally limited by the indirectness of the process, by the fact that the user is not physically executing the task. In this work, we study intelligent and customizable interfaces: these are interfaces that mediate the consequences of indirectness and make teleoperation more seamless. They are intelligent in that they take advantage of the robot's autonomous capabilities and assist in accomplishing the task. They are customizable in that ...
Rrt*-Ar: Sampling-Based Alternate Routes Planning With Applications To Autonomous Emergency Landing Of A Helicopter, Sanjiban Choudhury, Sebastian Scherer, Sanjiv Singh Carnegie Mellon University
Rrt*-Ar: Sampling-Based Alternate Routes Planning With Applications To Autonomous Emergency Landing Of A Helicopter, Sanjiban Choudhury, Sebastian Scherer, Sanjiv Singh
Robotics Institute
Engine malfunctions during helicopter flight poses a large risk to pilot and crew. Without a quick and coordinated reaction, such situations lead to a complete loss of control. An autonomous landing system could react quicker to regain control, however current emergency landing methods only generate dynamically feasible trajectories without considering obstacles. We address the problem of autonomously landing a helicopter while considering a realistic context: multiple potential landing zones, geographical terrain, sensor limitations and pilot contextual knowledge. We designed a planning system to generate alternate routes (AR) that respect these factors till touchdown exploiting the human-in-loop to make a choice ...
Sparse Tangential Network (Spartan): Motion Planning For Micro Aerial Vehicles, Hugh Cover, Sanjiban Choudhury, Sebastian Scherer, Sanjiv Singh Carnegie Mellon University
Sparse Tangential Network (Spartan): Motion Planning For Micro Aerial Vehicles, Hugh Cover, Sanjiban Choudhury, Sebastian Scherer, Sanjiv Singh
Robotics Institute
Micro aerial vehicles operating outdoors must be able to maneuver through both dense vegetation and across empty fields. Existing approaches do not exploit the nature of such an environment. We have designed an algorithm which plans rapidly through free space and is efficiently guided around obstacles. In this paper we present SPARTAN (Sparse Tangential Network) as an approach to create a sparsely connected graph across a tangential surface around obstacles. We find that SPARTAN can navigate a vehicle autonomously through an outdoor environment producing plans 172 times faster than the state of the art (RRT*). As a result SPARTAN can ...
An Analysis Of Simultaneous Localization And Mapping (Slam) Algorithms, Megan R. Naminski 
Macalester College
An Analysis Of Simultaneous Localization And Mapping (Slam) Algorithms, Megan R. Naminski
Honors Projects
This paper provides an introduction to two Simultaneous Localization and Mapping (SLAM) algorithms: EKF SLAM and Fast-SLAM. SLAM allows an autonomous robot to accurately map an unknown environment as well as locate itself within the environment. These algorithms work iteratively, by moving about the environment and extracting and observing various landmarks in the environment. EKF SLAM and Fast-SLAM solve the SLAM problem by using probabilities to control for errors in the robot's sensors. This paper provides a discussion of these two algorithms and compares their run times and the accuracy of the maps they produce.
Robust Monocular Visual Odometry For A Ground Vehicle In Undulating Terrain, Ji Zhang, Sanjiv Singh, George Kantor Carnegie Mellon University
Robust Monocular Visual Odometry For A Ground Vehicle In Undulating Terrain, Ji Zhang, Sanjiv Singh, George Kantor
Robotics Institute
Here we present a robust method for monocular visual odometry capable of accurate position estimation even when operating in undulating terrain. Our algorithm uses a steering model to separately recover rotation and translation. Robot 3DOF orientation is recovered by minimizing image projection error, while, robot translation is recovered by solving an NP-hard optimization problem through an approximation. The decoupled estimation ensures a low computational cost. The proposed method handles undulating terrain by approximating ground patches as locally flat but not necessarily level, and recovers the inclination angle of the local ground in motion estimation. Also, it can automatically detect when ...
Reconstructing 3d Human Pose From 2d Image Landmarks, Varun Ramakrishna, Takeo Kanade, Yaser Sheikh Carnegie Mellon University
Reconstructing 3d Human Pose From 2d Image Landmarks, Varun Ramakrishna, Takeo Kanade, Yaser Sheikh
Robotics Institute
Reconstructing an arbitrary configuration of 3D points from their projection in an image is an ill-posed problem. When the points hold semantic meaning, such as anatomical landmarks on a body, human observers can often infer a plausible 3D configuration, drawing on extensive visual memory. We present an activity-independent method to recover the 3D configuration of a human figure from 2D locations of anatomical landmarks in a single image, leveraging a large motion capture corpus as a proxy for visual memory. Our method solves for anthropometrically regular body pose and explicitly estimates the camera via a matching pursuit algorithm operating on ...
Infrastructure-Free Shipdeck Tracking For Autonomous Landing, Sankalp Arora, Sezal Jain, Sebastian Scherer, Stephen Nuske, Lyle J. Chamberlain, Sanjiv Singh Carnegie Mellon University
Infrastructure-Free Shipdeck Tracking For Autonomous Landing, Sankalp Arora, Sezal Jain, Sebastian Scherer, Stephen Nuske, Lyle J. Chamberlain, Sanjiv Singh
Robotics Institute
Shipdeck landing is one of the most challenging tasks for a rotorcraft. Current autonomous rotorcraft use shipdeck mounted transponders to measure the relative pose of the vehicle to the landing pad. This tracking system is not only expensive but renders an unequipped ship unlandable. We address the challenge of tracking shipdeck without additional infrastructure on the deck. We present two methods based on video and lidar that are able to track the shipdeck starting at a considerable distance from the ship. This redundant sensor design enables us to have two independent tracking systems. We show the results of the tracking ...
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