Ross A. Knepper

RF-compass: robot object manipulation using RFIDs

Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the objects orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches). This paper presents RF-Compass, an RFID-based system for robot navigation and object manipulation. RFIDs are low-cost and work in non-line-of-sight scenarios, allowing them to address the limitations of existing solutions. Given an RFID-tagged object, RF-Compass accurately navigates a robot equipped with RFIDs toward the object. Further, it locates the center of the object to within a few centimeters and identifies its orientation so that the robot may pick it up. RF-Compasss key innovation is an iterative algorithm formulated as a convex optimization problem. The algorithm uses the RFID signals to partition the space and keeps refining the partitions based on the robots consecutive moves.We have implemented RF-Compass using USRP software radios and evaluated it with commercial RFIDs and a KUKA youBot robot. For the task of furniture assembly, RF-Compass can locate furniture parts to a median of 1.28 cm, and identify their orientation to a median of 3.3 degrees.

Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home

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, Pittsburgh, PA. 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 the particular challenges on working in human spaces, and describe some of the lessons we learned from extensively testing our integrated platform in kitchen and office environments.

IkeaBot: An autonomous multi-robot coordinated furniture assembly system

We present an automated assembly system that directs the actions of a team of heterogeneous robots in the completion of an assembly task. From an initial user-supplied geometric specification, the system applies reasoning about the geometry of individual parts in order to deduce how they fit together. The task is then automatically transformed to a symbolic description of the assembly-a sort of blueprint. A symbolic planner generates an assembly sequence that can be executed by a team of collaborating robots. Each robot fulfills one of two roles: parts delivery or parts assembly. The latter are equipped with specialized tools to aid in the assembly process. Additionally, the robots engage in coordinated co-manipulation of large, heavy assemblies. We provide details of an example furniture kit assembled by the system.

DeepMPC: Learning Deep Latent Features for Model Predictive Control.

Designing controllers for tasks with complex nonlinear dynamics is extremely challenging, time-consuming, and in many cases, infeasible. This difficulty is exacerbated in tasks such as robotic food-cutting, in which dynamics might vary both with environmental properties, such as material and tool class, and with time while acting. In this work, we present DeepMPC, an online real-time model-predictive control approach designed to handle such difficult tasks. Rather than hand-design a dynamics model for the task, our approach uses a novel deep architecture and learning algorithm, learning controllers for complex tasks directly from data. We validate our method in experiments on a large-scale dataset of 1488 material cuts for 20 diverse classes, and in 450 real-world robotic experiments, demonstrating significant improvement over several other approaches.

Asking for Help Using Inverse Semantics.

Robots inevitably fail, often without the ability to recover autonomously. We demonstrate an approach for enabling a robot to recover from failures by communicating its need for specific help to a human partner using natural language. Our approach automatically detects failures, then generates targeted spoken-language requests for help such as “Please give me the white table leg that is on the black table.” Once the human partner has repaired the failure condition, the system resumes full autonomy. We present a novel inverse semantics algorithm for generating effective help requests. In contrast to forward semantic models that interpret natural language in terms of robot actions and perception, our inverse semantics algorithm generates requests by emulating the human’s ability to interpret a request using the Generalized Grounding Graph (G) framework. To assess the effectiveness of our approach, we present a corpusbased online evaluation, as well as an end-to-end user study, demonstrating that our approach increases the effectiveness of human interventions compared to static requests for help.

High Performance State Lattice Planning Using Heuristic Look-Up Tables

This paper presents a solution to the problem of finding an effective yet admissible heuristic function for A* by precomputing a look-up table of solutions. This is necessary because traditional heuristic functions such as Euclidean distance often produce poor performance for certain problems. Here, the technique is applied to the state lattice, which is used for full state space motion planning. However, the approach is applicable to many applications of heuristic search algorithms. The look-up table is demonstrated to be feasible to generate and store. A principled technique is presented for selecting which queries belong in the table. Finally, the results are validated through testing on a variety of path planning problems

Path and trajectory diversity: Theory and algorithms

We present heuristic algorithms for pruning large sets of candidate paths or trajectories down to smaller subsets that maintain desirable characteristics in terms of overall reachability and path length. Consider the example of a set of candidate paths in an environment that is the result of a forward search tree built over a set of actions or behaviors. The tree is precomputed and stored in memory to be used online to compute collision-free paths from the root of the tree to a particular goal node. In general, such a set of paths may be quite large, growing exponentially in the depth of the search tree. In practice, however, many of these paths may be close together and could be pruned without a loss to the overall problem of path-finding. The best such pruning for a given resulting tree size is the one that maximizes path diversity, which is quantified as the probability of the survival of paths, averaged over all possible obstacle environments. We formalize this notion and provide formulas for computing it exactly. We also present experimental results for two approximate algorithms for path set reduction that are efficient and yield desirable properties in terms of overall path diversity. The exact formulas and approximate algorithms generalize to the computation and maximization of spatio-temporal diversity for trajectories.

Model-Predictive Motion Planning: Several Key Developments for Autonomous Mobile Robots

A necessary attribute of a mobile robot planning algorithm is the ability to accurately predict the consequences of robot actions to make informed decisions about where and how to drive. It is also important that such methods are efficient, as onboard computational resources are typically limited and fast planning rates are often required. In this article, we present several practical mobile robot motion planning algorithms for local and global search, developed with a common underlying trajectory generation framework for use in model-predictive control. These techniques all center on the idea of generating informed, feasible graphs at scales and resolutions that respect computational and temporal constraints of the application. Connectivity in these graphs is provided by a trajectory generator that searches in a parameterized space of robot inputs subject to an arbitrary predictive motion model. Local search graphs connect the currently observed state-to-states at or near the planning or perception horizon. Global search graphs repeatedly expand a precomputed trajectory library in a uniformly distributed state lattice to form a recombinant search space that respects differential constraints. In this article, we discuss the trajectory generation algorithm, methods for online or offline calibration of predictive motion models, sampling strategies for local search graphs that exploit global guidance and environmental information for real-time obstacle avoidance and navigation, and methods for efficient design of global search graphs with attention to optimality, feasibility, and computational complexity of heuristic search. The model-invariant nature of our approach to local and global motions planning has enabled a rapid and successful application of these techniques to a variety of platforms. Throughout the article, we also review experiments performed on planetary rovers, field robots, mobile manipulators, and autonomous automobiles and discuss future directions of the article.

Hierarchical planning architectures for mobile manipulation tasks in indoor environments

This paper describes a hierarchical planner deployed on a mobile manipulation system. The main idea is a two-level hierarchy combining a global planner which provides rough guidance to a local planner. We place a premium on fast response, so the global planner achieves speed by using a very rough approximation of the robot kinematics, and the local planner begins execution of the next action even without considering subsequent actions in detail, instead relying on the guidance of the global planner. The system exhibits few planning delays, and yet is surprisingly effective at planning collision free motions. The system is deployed on HERB [20], combining a Segway mobile platform, a WAM arm, and a Barrett hand. The navigation and manipulation components have been tested on the real robot, and the task of simultaneously approaching and grasping a bottle on a countertop was demonstrated in simulation.

Path diversity is only part of the problem

The goal of motion planning is to find a feasible path that connects two positions and is free from collision with obstacles. Path sets are a robust approach to this problem in the face of real-world complexity and uncertainty. A path set is a collection of feasible paths and their corresponding control sequences. A path-set-based planner navigates by repeatedly testing each of these robot-fixed paths for collision with obstacles. A heuristic function selects which of the surviving paths to follow next. At each step, the robot follows a small piece of each path selected while simultaneously planning the subsequent trajectory. A path set possesses high path diversity if it performs well at obstacle-avoidance and goal-seeking behaviors. Previous work in path diversity has tacitly assumed that a correlation exists between this dynamic planning problem and a simpler, static path diversity problem: a robot placed randomly into an obstacle field evaluates its path set for collision a single time before following the chosen path in entirety. Although these problems might intuitively appear to be linked, this paper shows that static and dynamic path diversity are two distinct properties. After empirically demonstrating this fact, we discuss some of the factors that differentiate the two problems.