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Venkatraman Narayanan


I am a final year Ph.D. student in the Robotics Institute at Carnegie Mellon University, advised by Dr. Maxim Likhachev. My general research interests span Artificial Intelligence (AI), Perception, and Planning for Robotic Systems, including personal assistance robots, autonomous cars, unmanned aerial vehicles, and manipulators in warehouse automation and flexible manufacturing.

My thesis work on Deliberative Perception introduces and adapts classical AI techniques for reliable 3D robot perception, with focus on object recognition and pose estimation. Earlier, I developed motion planning algorithms for autonomous manipulation of articulated objects, navigation in dynamic environments, and navigation under topological constraints.

Central to our work on perception and planning are heuristic search algorithms that we actively research. For instance, we have developed multi-heuristic graph search techniques that allow algorithms like A* to scale-up to complex and high-dimensional graph search problems encountered in robotics.

I interned at the Uber Advanced Technologies Center in Summer '15 and with the self-driving car team at Google X in Summer '13.

Before coming to CMU, I obtained my undergraduate degree in Electronics and Communication Engineering from College of Engineering, Guindy (CEG), Anna University, India.

Venkatraman Narayanan

Office: Newell-Simon Hall 1612D
Email:
CV | Google Scholar | Github

Deliberative Perception


A recurrent and elementary machine perception task is to localize objects of interest in the physical world, be it objects on a warehouse shelf or cars on a road. In many real-world examples, this task entails localizing specific object instances with known 3D models. For example, a warehouse robot equipped with a depth sensor is required to recognize and localize objects in a shelf with known inventory, while a low-cost industrial robot might need to localize parts on an assembly line.

Most modern-day methods for the 3D multi-object localization task employ scene-to-model feature matching or regression/classification by learners trained on synthetic or real scenes. While these methods are typically fast in producing a result, they are often brittle, sensitive to occlusions, and depend on the right choice of features and/or training data. We introduce and advocate a deliberative approach, where the multi-object localization task is framed as an optimization over the space of hypothesized scenes. Our thesis is that deliberative reasoning--such as understanding inter-object occlusions--is essential to robust perception, and that the role of discriminative algorithms should mainly be to guide this process.

Multi-Heuristic Graph Search


Many problems encountered in robotics, be it in perception or planning, lend themselves naturally to graph search formulations. Traditional graph search algorithms such as A* require a single "admissible" heuristic to guarantee solution optimality. However, designing such heuristics by hand is often challenging and time-consuming. Consequently, it would be beneficial to use a suite of independent "weak" heuristics or modern learning techniques such as deep neural networks to learn heuristics to guide graph search. To this end, we have developed a family of algorithms titled "Multi-Heuristic A* for searching with multiple inadmissible heuristics (in conjunction with one admissible heuristic) without compromising solution quality guarantees. Finally, these are abstract graph search algorithms applicable in many domains--for instance, we have used these in the contexts of both Deliberative Perception and high-dimensional robot motion planning.

Manipulating Articulated Objects


Personal robots need to manipulate a variety of articulated mechanisms such as doors and drawers, 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. We make two 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.

Planning with Topological Constraints


For a UAV on a surveillance mission, what is the optimal path that would enable it to circumnavigate particular regions of interest? How can one find paths for a ground robot that satisfy some constraint with respect to obstacles in the environment? These problems can be formulated as planning with homology and homotopy constraints, or more generally, planning with topological constraints. We present a framework based on graph-search to solve these problems, by capturing topological information in a graph state variable.

Planning in 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. In this work, we develop an anytime planner that produces an initial solution quickly, and improves the quality of the solution as time permits. Additionally, by using 'safe intervals' rather than time as a state dimension, the planner can operate in real-time scenarios.













Journal Articles


  • Discriminatively-guided Deliberative Perception for Pose Estimation of Multiple 3D Object Instances
    Venkatraman Narayanan and Maxim Likhachev
    International Journal of Robotics Research (IJRR), 2017
    [invited submission from RSS 2016, under review]
  • Multi-Heuristic A*
    Sandip Aine, Siddharth Swaminathan, Venkatraman Narayanan, Victor Hwang, and Maxim Likhachev
    International Journal of Robotics Research (IJRR), 2016
    [invited submission from RSS 2014]
    [pdf | bib]

Peer-Reviewed Conference Publications


  • Heuristic Search on Graphs with Existence Priors for Expensive-to-Evaluate Edges
    Venkatraman Narayanan and Maxim Likhachev
    International Conference on Automated Planning and Scheduling (ICAPS), Pittsburgh, 2017
    [pdf | bib | code]
  • Learning to Avoid Local Minima in Planning for Static Environments
    Shivam Vats, Venkatraman Narayanan, and Maxim Likhachev
    International Conference on Automated Planning and Scheduling (ICAPS), Pittsburgh, 2017
    [pdf | bib]
  • Deliberative Object Pose Estimation in Clutter
    Venkatraman Narayanan and Maxim Likhachev
    IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017
    [pdf | bib]
  • Discriminatively-guided Deliberative Perception for Pose Estimation of Multiple 3D Object Instances
    Venkatraman Narayanan and Maxim Likhachev
    Robotics: Science and Systems (RSS), Ann Arbor, USA, 2016
    [pdf | bib | slides (.mp4) | poster (.pdf) | talk | code]
  • PERCH: Perception via Search for Multi-Object Recognition and Localization
    Venkatraman Narayanan and Maxim Likhachev
    IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016
    [pdf | bib | slides (.pdf) | poster (.pdf) | code]
  • A*-Connect: Bounded Suboptimal Bidirectional Search
    Fahad Islam, Venkatraman Narayanan, and Maxim Likhachev
    IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016
    [pdf | bib]
  • Improved Multi-Heuristic A* for Searching with Uncalibrated Heuristics
    Venkatraman Narayanan, Sandip Aine, and Maxim Likhachev
    International Symposium on Combinatorial Search (SoCS), Ein Gedi, Israel, 2015
    [pdf | bib | slides (.pdf) | slides (.key) | code]
  • Efficient Search with an Ensemble of Heuristics
    Mike Phillips, Venkatraman Narayanan, Sandip Aine, and Maxim Likhachev
    International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, 2015
    [pdf | bib]
  • Task-Oriented Planning for Manipulating Articulated Mechanisms Under Model Uncertainty
    Venkatraman Narayanan and Maxim Likhachev
    IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, 2015
    [pdf | bib | poster (.pdf) | poster (.key) | slides (.pdf) | code]
  • Dynamic Multi-Heuristic A*
    Fahad Islam, Venkatraman Narayanan, and Maxim Likhachev
    IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, 2015
    [pdf | bib]
  • Multi-Heuristic A*
    Sandip Aine, Siddharth Swaminathan, Venkatraman Narayanan, Victor Hwang, and Maxim Likhachev
    Robotics: Science and Systems (RSS), Berkeley, USA, 2014
    [pdf | talk | poster (.pdf) | poster (.key) | bib]
  • Motion Planning for Robotic Manipulators with Independent Wrist Joints
    Kalin Gochev, Venkatraman Narayanan, Benjamin Cohen, Alla Safonova, and Maxim Likhachev
    IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 2014
    [pdf | bib]
  • Planning Under Topological Constraints Using Beam Graphs
    Venkatraman Narayanan, Paul Vernaza, Maxim Likhachev, and Steven M. LaValle
    IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013
    [pdf | poster (.key) | slides (.pdf) | bib]
  • Anytime Safe Interval Path Planning for Dynamic Environments
    Venkatraman Narayanan, Mike Phillips, and Maxim Likhachev
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal, 2012
    [pdf | slides (.key) |slides (.mov) | bib]
  • Efficiently Finding Optimal Winding-Constrained Loops in the Plane
    Paul Vernaza, Venkatraman Narayanan, and Maxim Likhachev
    Robotics: Science and Systems (RSS), Sydney, Australia, 2012
    [pdf | bib]

Abstracts/Workshop Publications


  • PERCH: Perception via Search for Multi-Object Recognition and Localization
    Venkatraman Narayanan and Maxim Likhachev
    Two-page abstract in 1st Workshop on Object Understanding for Interaction
    International Conference on Computer Vision (ICCV), Santiago, Chile, 2015
    [pdf | poster (.pdf)]
  • Multi-Heuristic A*
    Sandip Aine, Siddharth Swaminathan, Venkatraman Narayanan, Victor Hwang, and Maxim Likhachev
    Two-page abstract in Proceedings of the International Symposium on Combinatorial Search (SoCS), Prague, Czech Republic, 2014
    (Best Poster Presentation Award)
    [pdf | poster (.pdf) | poster (.key)]
  • Efficiently Finding Optimal Winding-Constrained Loops in the Plane
    Paul Vernaza, Venkatraman Narayanan, and Maxim Likhachev
    Two-page abstract in Proceedings of the International Symposium on Combinatorial Search (SoCS), Niagara Falls, Canada, 2012
    [pdf]














Miscellaneous


As a cryptic crossword enthusiast, I developed THC Online, a web application to interactively solve crosswords published in the Indian daily 'The Hindu'.

I co-organized an online puzzle solving event, Riddles of the Sphinx, in my junior year of undergrad. If you like solving puzzles and web-hunts, you are welcome to try this one. Use 'anonymous' as your username and password when prompted for the same.