Author name cluster

Venkatraman Narayanan

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17 papers

2 author rows

ICRA Conference 2023 Conference Paper

EWareNet: Emotion-Aware Pedestrian Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation

Venkatraman Narayanan
Bala Murali Manoghar
Rama Prashanth RV
Aniket Bera

We present EWareNet, a novel intent and affect-aware social robot navigation algorithm among pedestrians. Our approach predicts the trajectory-based pedestrian intent from gait sequence, which is then used for intent-guided navigation taking into account social and proxemic constraints. We propose a transformer-based model that works on commodity RGB-D cameras mounted onto a moving robot. Our intent prediction routine is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. Our navigation scheme consists of a novel obstacle profile representation methodology that is dynamically adjusted based on the pedestrian pose, intent, and affect. The navigation scheme is based on a reinforcement learning algorithm that takes pedestrian intent and robot's impact on pedestrian intent into consideration, in addition to the environmental configuration. We outperform current state-of-art algorithms for intent prediction from 3D gaits.

ICRA Conference 2021 Conference Paper

Learning Panoptic Segmentation from Instance Contours

Sumanth Chennupati
Venkatraman Narayanan
Ganesh Sistu
Senthil Kumar Yogamani
Samir A. Rawashdeh

Panoptic Segmentation aims to provide an understanding of background (stuff) and instances of objects (things) at a pixel level. It combines the separate tasks of semantic segmentation (pixel level classification) and instance segmentation to build a single unified scene understanding task. Typically, panoptic segmentation is derived by combining semantic and instance segmentation tasks that are learned separately or jointly (multi-task networks). In general, instance segmentation networks are built by adding a foreground mask estimation layer on top of object detectors or using instance clustering methods that assign a pixel to an instance center. In this work, we present a fully convolution neural network that learns instance segmentation from semantic segmentation and instance contours (boundaries of things). Instance contours along with semantic segmentation yield a boundary aware semantic segmentation of things. Connected component labeling on these results produces instance segmentation. We merge semantic and instance segmentation results to output panoptic segmentation. We evaluate our proposed method on the CityScapes dataset to demonstrate qualitative and quantitative performances along with several ablation studies. Our overview video can be accessed from https://youtu.be/wBtcxRhG3e0.

IROS Conference 2020 Conference Paper

ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation

Venkatraman Narayanan
Bala Murali Manoghar
Vishnu Sashank Dorbala
Dinesh Manocha
Aniket Bera

We present ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware robot navigation among pedestrians. Our approach predicts the perceived emotions of a pedestrian from walking gaits, which is then used for emotion-guided navigation taking into account social and proxemic constraints. To classify emotions, we propose a multi-view skeleton graph convolution-based model that works on a commodity camera mounted onto a moving robot. Our emotion recognition is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. It achieves a mean average emotion prediction precision of 82. 47% on the Emotion-Gait benchmark dataset. We outperform current state-of-art algorithms for emotion recognition from 3D gaits. We highlight its benefits in terms of navigation in indoor scenes using a Clearpath Jackal robot.

ICRA Conference 2017 Conference Paper

Deliberative object pose estimation in clutter

Venkatraman Narayanan
Maxim Likhachev

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 University's Team HARP in the 2016 Amazon Picking Challenge.

ICAPS Conference 2017 Conference Paper

Heuristic Search on Graphs with Existence Priors for Expensive-to-Evaluate Edges

Venkatraman Narayanan
Maxim Likhachev

We address the problem of finding shortest paths in graphs where some edges have a prior probability of existence, and their existence can be verified during planning with time- consuming operations. Our work is motivated by real-world robot motion planning, where edge existence is often expensive to verify (typically involves time-consuming collision-checking between the robot and world models), but edge existence probabilities are readily available. The goal then, is to develop an anytime algorithm that can return good solutions quickly by somehow leveraging the existence probabilities, and continue to return better-quality solutions or provide tighter suboptimality bounds with more time. While our motivation is fast and high-quality motion planning for robots, this work presents two fundamental contributions applicable to generic graphs with probabilistic edges. They are: a) an algorithm for efficiently computing all relevant shortest paths in a graph with probabilistic edges, and as a by-product the expected shortest path cost, and b) an anytime algorithm for evaluating (verifying existence of) edges in a collection of paths, which is optimal in expectation under a chosen distribution of the algorithm interruption time. Finally, we provide a practical approach to integrate a) and b) in the context of robot motion planning and demonstrate significant improvements in success rate and planning time for a 11 degree-of-freedom mobile manipulation planning problem. We also conduct additional evaluations on a 2D grid navigation domain to study our algorithm’s behavior.

ICAPS Conference 2017 Conference Paper

Learning to Avoid Local Minima in Planning for Static Environments

Shivam Vats
Venkatraman Narayanan
Maxim Likhachev

In many robot motion planning problems such as manipulation planning for a personal robot in a kitchen or an industrial manipulator in a warehouse, all motion planning queries are in an environment that is largely static. Consequently, one should be able to improve the performance of a planning algorithm by training on this static environment ahead of operation time. In this work, we propose a method to improve the performance of heuristic search-based motion planners in such environments. The first, learning, phase of our proposed method analyzes search performance on multiple planning episodes to infer local minima zones, that is, regions where the existing heuristic(s) are weakly correlated with the true cost-to-go. Then, in the planning phase of the method, the learnt local minima are used to modify the original search graph in a way that improves search performance. We prove that our method preserves guarantees on completeness and bounded suboptimality with respect to the original search graph. Experimentally, we observe significant improvements in success rate and planning time for challenging 11 degree-of-freedom mobile manipulation problems.

ICRA Conference 2016 Conference Paper

A*-Connect: Bounded suboptimal bidirectional heuristic search

Fahad Islam 0002
Venkatraman Narayanan
Maxim Likhachev

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.

ICRA Conference 2016 Conference Paper

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

Venkatraman Narayanan
Maxim Likhachev

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.

ICRA Conference 2015 Conference Paper

Dynamic Multi-Heuristic A*

Fahad Islam 0002
Venkatraman Narayanan
Maxim Likhachev

Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the configuration space has several ‘narrow passages’. Search-based planners on the other hand typically provide good solution qualities and are not affected by narrow passages. However, in the absence of a good heuristic or when there are deep local minima in the heuristic, they suffer from the curse of dimensionality. In this work, our primary contribution is a method for dynamically generating heuristics, in addition to the original heuristic(s) used, to guide the search out of local minima. With the ability to escape local minima easily, the effect of dimensionality becomes less pronounced. On the theoretical side, we provide guarantees on completeness and bounds on suboptimality of the solution found. We compare our proposed method with the recently published Multi-Heuristic A* search, and the popular RRT-Connect in a full-body mobile manipulation domain for the PR2 robot, and show its benefits over these approaches.

IJCAI Conference 2015 Conference Paper

Efficient Search with an Ensemble of Heuristics

Mike Phillips
Venkatraman Narayanan
Sandip Aine
Maxim Likhachev

Recently, a number of papers have shown that for many domains, using multiple heuristics in independent searches performs better than combining them into a single heuristic. Furthermore, using a large number of “weak” heuristics could potentially eliminate the need for the careful design of a few. The standard approach to distribute computation in these multi-heuristic searches is to rotate through the heuristics in a round-robin fashion. However, this strategy can be inefficient especially in the case when only a few of the heuristics are leading to progress. In this paper, we present two principled methods to adaptively distribute computation time among the different searches of the Multi- Heuristic A* algorithm. The first method, Meta-A*, constructs and searches a meta-graph, which represents the problem of finding the best heuristic as the problem of minimizing the total number of expansions. The second treats the scheduling of searches with different heuristics as a multi-armed bandit problem. It applies Dynamic Thompson Sampling (DTS) to keep track of what searches are making progress the most and continuously re-computes the schedule of searches based on this information. We provide a theoretical analysis and compare our new strategies with the round-robin method on a 12-DOF full-body motion planning problem and on sliding tile puzzle problems. In these experiments, we used up to 20 heuristics and observed a several times speedup without loss in solution quality.

SoCS Conference 2015 Conference Paper

Improved Multi-Heuristic A* for Searching with Uncalibrated Heuristics

Venkatraman Narayanan
Sandip Aine
Maxim Likhachev

Recently, several researchers have brought forth the benefits of searching with multiple (and possibly inadmissible) heuristics, arguing how different heuristics could be independently useful in different parts of the state space. However, algorithms that use inadmissible heuristics in the traditional best-first sense, such as the recently developed Multi-Heuristic A* (MHA*), are subject to a crippling calibration problem: they prioritize nodes for expansion by additively combining the cost-to-come and the inadmissible heuristics even if those heuristics have no connection with the cost-to-go (e. g. , the heuristics are uncalibrated). For instance, if the inadmissible heuristic were an order of magnitude greater than the perfect heuristic, an algorithm like MHA* would simply reduce to a weighted A* search with one consistent heuristic. In this work, we introduce a general multi-heuristic search framework that solves the calibration problem and as a result a) facilitates the effective use of multiple uncalibrated inadmissible heuristics, and b) provides significantly better performance than MHA* whenever tighter sub-optimality bounds on solution quality are desired. Experimental evaluations on a complex full-body robotics motion planning problem and large sliding tile puzzles demonstrate the benefits of our framework.

ICRA Conference 2015 Conference Paper

Task-oriented planning for manipulating articulated mechanisms under model uncertainty

Venkatraman Narayanan
Maxim Likhachev

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.

ICRA Conference 2014 Conference Paper

Motion planning for robotic manipulators with independent wrist joints

Kalin Gochev
Venkatraman Narayanan
Benjamin J. Cohen
Alla Safonova
Maxim Likhachev

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 Garage's 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.

SoCS Conference 2014 Conference Paper

Multi-Heuristic A

Sandip Aine
Siddharth Swaminathan
Venkatraman Narayanan
Victor Hwang
Maxim Likhachev

We present a novel heuristic search framework, called Multi-Heuristic A* (MHA*), that simultaneously uses multiple, arbitrarily inadmissible heuristic functions and one consistent heuristic to search for complete and bounded suboptimal solutions. This simplifies the de- sign of heuristics and enables the search to effectively combine the guiding powers of different heuristic func- tions. We support these claims with experimental results on full-body manipulation for PR2 robots.

ICRA Conference 2013 Conference Paper

Planning under topological constraints using beam-graphs

Venkatraman Narayanan
Paul Vernaza
Maxim Likhachev
Steven M. LaValle

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.

IROS Conference 2012 Conference Paper

Anytime Safe Interval Path Planning for dynamic environments

Venkatraman Narayanan
Mike Phillips
Maxim Likhachev

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.

SoCS Conference 2012 Conference Paper

Efficiently Finding Optimal Winding-Constrained Loops in the Plane: Extended Abstract

Paul Vernaza
Venkatraman Narayanan
Maxim Likhachev

We present a method to efficiently find winding-constrained loops in the plane that are optimal with respect to a minimum- cost 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 in- terpretation 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.