Venkatraman Narayanan

Anytime Safe Interval Path Planning for dynamic environments

Path planning in dynamic environments is significantly more difficult than navigation in static spaces due to the increased dimensionality of the problem, as well as the importance of returning good paths under time constraints. Anytime planners are ideal for these types of problems as they find an initial solution quickly and then improve it as time allows. In this paper, we develop an anytime planner that builds off of Safe Interval Path Planning (SIPP), which is a fast A*-variant for planning in dynamic environments that uses intervals instead of timesteps to represent the time dimension of the problem. In addition, we introduce an optional time-horizon after which the planner drops time as a dimension. On the theoretical side, we show that in the absence of time-horizon our planner can provide guarantees on completeness as well as bounds on the sub-optimality of the solution with respect to the original space-time graph. We also provide simulation experiments for planning for a UAV among 50 dynamic obstacles, where we can provide safe paths for the next 15 seconds of execution within 0.05 seconds. Our results provide a strong evidence for our planner working under real-time constraints.

PERCH: Perception via search for multi-object recognition and localization

In many robotic domains such as flexible automated manufacturing or personal assistance, a fundamental perception task is that of identifying and localizing objects whose 3D models are known. Canonical approaches to this problem include discriminative methods that find correspondences between feature descriptors computed over the model and observed data. While these methods have been employed successfully, they can be unreliable when the feature descriptors fail to capture variations in observed data; a classic cause being occlusion. As a step towards deliberative reasoning, we present PERCH: PErception via SeaRCH, an algorithm that seeks to find the best explanation of the observed sensor data by hypothesizing possible scenes in a generative fashion. Our contributions are: i) formulating the multi-object recognition and localization task as an optimization problem over the space of hypothesized scenes, ii) exploiting structure in the optimization to cast it as a combinatorial search problem on what we call the Monotone Scene Generation Tree, and iii) leveraging parallelization and recent advances in multi-heuristic search in making combinatorial search tractable. We prove that our system can guaranteedly produce the best explanation of the scene under the chosen cost function, and validate our claims on real world RGB-D test data. Our experimental results show that we can identify and localize objects under heavy occlusion- cases where state-of-the-art methods struggle.

Planning under topological constraints using beam-graphs

We present a framework based on graph search for navigation in the plane with a variety of topological constraints. The method is based on modifying a standard graph-based navigation approach to keep an additional state variable that encodes topological information about the path. The topological information is represented by a sequence of virtual sensor beam crossings. By considering classes of beam crossing sequences to be equivalent under certain equivalence relations, we obtain a general method for planning with topological constraints that subsumes existing approaches while admitting more favorable representational characteristics. We provide experimental results that validate the approach and show how the planner can be used to find loop paths for autonomous surveillance problems, simultaneously satisfying minimum-cost objectives and in dynamic environments. As an additional application, we demonstrate the use of our planner on the PR2 robot for automated building of 3D object models.

Multi-Heuristic A*

The performance of heuristic search-based planners depends heavily on the quality of the heuristic function used to focus the search. These algorithms work fast and generate high-quality solutions, even for high-dimensional problems, as long as they are given a well-designed heuristic function. On the other hand, their performance can degrade considerably if there are large heuristic depression regions, i.e. regions in the search space where heuristic values do not correlate well with the actual cost-to-goal values. Consequently, the research in developing an efficient planner for a specific domain becomes the design of a good heuristic function. However, for many domains, it is hard to design a single heuristic function that captures all of the complexities of the problem. Furthermore, it is hard to ensure that heuristics are admissible (provide lower bounds on the cost-to-goal) and consistent, which is necessary for A* like searches to provide guarantees on completeness and bounds on sub-optimality. In this paper, we develop a novel heuristic search, called Multi-Heuristic A* (MHA*), that takes in multiple, arbitrarily inadmissible heuristic functions in addition to a single consistent heuristic, and uses all of them simultaneously to search in a way that preserves guarantees on completeness and bounds on sub-optimality. This enables the search to combine very effectively the guiding powers of different heuristic functions and simplifies dramatically the process of designing heuristic functions by a user because these functions no longer need to be admissible or consistent. We support these claims with experimental analysis on several domains ranging from inherently continuous domains such as full-body manipulation and navigation to inherently discrete domains such as the sliding tile puzzle.

Efficiently finding optimal winding-constrained loops in the plane

We present a method to efficiently find winding-constrained loops in the plane that are optimal with respect to a minimumcost objective and in the presence of obstacles. Our approach is similar to a typical graph-based search for an optimal path in the plane, but with an additional state variable that encodes information about path homotopy. Upon finding a loop, the value of this state corresponds to a line integral over the loop that indicates how many times it winds around each obstacle, enabling us to reduce the problem of finding paths satisfying winding constraints to that of searching for paths to suitable states in this augmented state space. We give an intuitive interpretation of the method based on fluid mechanics and show how this yields a way to perform the necessary calculations efficiently. Results are given in which we use our method to find optimal routes for autonomous surveillance and intruder containment.

Discriminatively-guided Deliberative Perception for Pose Estimation of Multiple 3D Object Instances

We introduce a novel paradigm for model-based multi-object recognition and 3 DoF pose estimation from 3D sensor data that integrates exhaustive global reasoning with discriminatively-trained algorithms in a principled fashion. Typical approaches for this task are based on scene-to-model feature matching or regression by statistical learners trained on a large database of annotated scenes. These approaches are fast but sensitive to occlusions, features, and/or training data. Generative approaches, on the other hand, e.g., methods based on rendering and verification, are robust to occlusions and require no training, but are slow at test time. We conjecture that robust and efficient perception can be achieved through a combination of generative methods and discriminatively-trained approaches. To this end, we introduce the Discriminatively-guided Deliberative Perception (D2P) paradigm that has the following desirable properties: a) D2P is a single search algorithm that looks for the ‘best’ rendering of the scene that matches the input, b) can be guided by any and multiple discriminative algorithms, and c) generates a solution that is provably bounded suboptimal with respect to the chosen cost function. In addition, we introduce the notions of completeness and resolution completeness for multi-object pose estimation problems, and show that D2P is resolution complete. We conduct extensive evaluations on a benchmark dataset to study various aspects of D2P in relation to existing approaches.

Deliberative object pose estimation in clutter

A fundamental robot perception task is that of identifying and estimating the poses of objects with known 3D models in RGB-D data. While feature-based and discriminative approaches have been traditionally used for this task, recent work on deliberative approaches such as PERCH and D2P have shown improved robustness in handling scenes with severe inter-object occlusions. These deliberative approaches work by treating multi-object pose estimation as a combinatorial search over the space of possible rendered scenes of the objects, thereby inherently being able to predict and account for occlusions. However, these methods have so far been restricted to scenes comprising only of known objects, and have been unable to handle extraneous clutter — a common occurrence in many real-world settings. This work significantly increases the practical relevance of deliberative perception methods by developing a formulation that: i) accounts for extraneous unmodeled clutter in scenes, and ii) provides object pose uncertainty estimates. Our algorithm is complete and provides bounded suboptimality guarantees for the cost function chosen to be optimized. Empirically, we demonstrate successful object recognition and uncertainty-aware localization in challenging scenes with unmodeled clutter, where previous deliberative methods perform unsatisfactorily. In addition, this work was used as part of the perception system by Carnegie Mellon Universitys Team HARP in the 2016 Amazon Picking Challenge.

A*-Connect: Bounded suboptimal bidirectional heuristic search

The benefits of bidirectional planning over the unidirectional version are well established for motion planning in high-dimensional configuration spaces. While bidirectional approaches have been employed with great success in the context of sampling-based planners such as in RRT-Connect, they have not enjoyed popularity amongst search-based methods such as A*. The systematic nature of search-based algorithms, which often leads to consistent and high-quality paths, also enforces strict conditions for the connection of forward and backward searches. Admissible heuristics for the connection of forward and backward searches have been developed, but their computational complexity is a deterrent. In this work, we leverage recent advances in search with inadmissible heuristics to develop an algorithm called A*-Connect, much in the spirit of RRT-Connect. A*-Connect uses a fast approximation of the classic front-to-front heuristic from literature to lead the forward and backward searches towards each other, while retaining theoretical guarantees on completeness and bounded suboptimality. We validate A*-Connect on manipulation as well as navigation domains, comparing with popular sampling-based methods as well as state-of-the-art bidirectional search algorithms. Our results indicate that A*-Connect can provide several times speedup over unidirectional search while maintaining high solution quality.

Motion planning for robotic manipulators with independent wrist joints

Advanced modern humanoid robots often have complex manipulators with a large number of degrees of freedom. Thus, motion planning for such manipulators is a very computationally challenging problem. However, often robotic manipulators allow the wrist degrees of freedom to be controlled independently from the configuration of the rest of the arm. In this paper we show how to split the high dimensional planning problem into two lower-dimensional sub-problems - planning for the main arm joints and planning for the wrist joints, without losing guarantees on completeness. This approach is an extension of our previously developed framework for planning with adaptive dimensionality. Experimentally, we show that this approach is very effective in speeding up planning for robotic arms on Willow Garages PR2 platform. We compare our algorithm with several popular alternative approaches for performing motion planning for robotic arms. The results we observe illustrate that our algorithm provides a good balance between planning time, planning success rate, path consistency, and path quality.

Task-oriented planning for manipulating articulated mechanisms under model uncertainty

Personal robots need to manipulate a variety of articulated mechanisms as part of day-to-day tasks. These tasks are often specific, goal-driven, and permit very little bootstrap time for learning the articulation type. In this work, we address the problem of purposefully manipulating an articulated object, with uncertainty in the type of articulation. To this end, we provide two primary contributions: first, an efficient planning algorithm that, given a set of candidate articulation models, is able to correctly identify the underlying model and simultaneously complete a task; and second, a representation for articulated objects called the Generalized Kinematic Graph (GK-Graph), that allows for modeling complex mechanisms whose articulation varies as a function of the state space. Finally, we provide a practical method to auto-generate candidate articulation models from RGB-D data and present extensive results on the PR2 robot to demonstrate the utility of our representation and the efficiency of our planner.