/Maxim Likhachev

Maxim Likhachev

Portrait of Maxim Likhachev
Associate Professor
Office: 3211 Newell-Simon Hall
Phone: (412) 268-5581
Personal Homepage
Administrative Assistant: Peggy A. Martin
Lab: Search-based Planning Laboratory

Mailing Address

My general research interests lie in Artificial Intelligence and Robotics. More specifically, they currently cover planning in deterministic and probabilistic domains and machine learning. My research has been mainly motivated by the problem of fast and intelligent decision making by autonomous robotic systems operating in real-world environments. Some of the robotic systems my group does planning for include mobile manipulators, aerial vehicles, multi-robot systems, and humanoid-like robots. I do get easily motivated, however, by other interesting planning problems.

 

 

My long-term research goal is to develop a methodology for robust real-time decision-making in autonomous systems. To achieve this goal, my students and I research novel decision-making algorithms and use these algorithms to build planning modules that enable complex robotic systems to operate autonomously. Our approach is currently based on pushing the limits of graph search- based planning. Conventional wisdom in the robotics community is that graph search approaches cannot provide real-time performance guarantees, do not scale to higher-dimensional problems, and cannot deal with problems that involve uncertainty. My group develops graph search algorithms that are capable of solving challenging problems in robotics in real-time while still maintaining all the positive properties of graph search algorithms such as generality, cost minimization and rigorous guarantees on completeness and quality of solutions. We then use these algorithms to build real-time planners and demonstrate them on physical robots performing such tasks as autonomous navigation, autonomous flight and landing, autonomous mobile manipulation and others.

Through our work on building actual planners for physical robots, we have found that it is im- portant to link tightly research on graph search algorithms with the work on deriving the “right” representations of planning problems. The representation needs to encode the planning problem in a way that facilitates both efficient search for a solution and robust execution of the solution by a robot. For example, motion planning for a car-like robot cannot use graphs derived from simple grids. Planning on such graphs assumes the ability to turn in-place and renders the generated plans infeasible to execute for a car-like robot. Our experience has shown that careful thinking about the representation leads to a better understanding of what are the real challenges that need to be addressed in building an effective planner. This knowledge drives our research into developing novel graph search techniques that overcome these challenges. Also, finding the “right” graph representa- tion can often be combined with the problem of searching the graph. Studying the combined problem can lead to a highly effective solution to the overall planning problem.

All in all, I strive to maintain the research environment in my lab that is unique in that we both, do highly algorithmic work on developing novel graph search algorithms and work closely with physical robots and draw inspiration from this work. For example, I have co-developed well-known incremental and anytime graph search algorithms such as D* Lite [16], ARA* [22] and Anytime D* [21], my group pioneered the Experience Graph [27], a framework that enables heuristic search algorithms to improve their runtimes by solving similar problems, and we have just developed Multi-

(a) dual-arm (b) full-body (c) industrial (d) assembly

Figure 1: Our work on developing novel graph search algorithms and compact graph representations and applying them to high-dimensional planning for a wide range of mobile manipulation tasks.

Heuristic A* [3], the first heuristic search to handle multiple (possibly many) inadmissible heuristics without losing its theoretical guarantees. At the same time, my group built planners for such impressive systems as an industrial mobile manipulator used for paint stripping different aircrafts (Figure 1(c)) – the project that won the national Gold Edison award in 2013 – a full-size K-MAX helicopter (Figure 2(b)) and a full-size SUV (Figure 2(c)) that won the DARPA Urban Challenge race in 2007 [20]. By building such planners, we push forward the frontier of robotics and change how many of the planning problems in robotics are approached.

In the following, I give a few examples from my research. I first briefly mention several research themes that cut through most of the work my group and I have done in the past. Afterwards, I give several more examples that describe some of the latest research directions we have been pursuing.

• Graph search-based planning for solutions with bounded sub-optimality. While find- ing a provably optimal path in a high-dimensional search-space is computationally intractable, for many planning problems in robotics, allowing even a small amount of sub-optimality in the solution allows the search to quickly find high-quality solutions. We have exploited this observation to develop a number of graph search algorithms that allow the trade-off of solu- tion quality for fast planning time including an anytime version of A*, ARA* [22], Anytime SIPP [26, 29] for planning in dynamic environments and Planning with Adaptive Dimension- ality [11, 13]. Together with my students and colleagues, we have used these searches to build highly effective planners for high-dimensional robotic systems ranging from single-arm and dual-arm manipulation [8] to full-body manipulation on PR2 [6] (Figure 1(a,b)) and on a large mobile manipulator built to strip paint off airplanes autonomously (Figure 1(c)).

• Decomposition of hard planning problems into a series of easy-to-solve graph searches. Another observation my research exploits is that many seemingly difficult planning problems in robotics can often be decomposed into a series of easy-to-solve graph searches. The solutions found by these searches can be combined to obtain solutions to the original problems with rigorous theoretical guarantees. Based on this observation, we have developed a number of algorithms including R* [23] for high-dimensional planning, Probabilistic Planning with Clear Preferences (PPCP) [24] for planning under uncertainty in the environment and Distributed Path Consensus (DPC) [5] for multi-robot planning. We have then used these algorithms to build planners that can run on-board robotic systems. For example, PPCP was run on-board a small autonomous quadrotor built by my students to compute an optimal landing site selection policy under uncertainty in landing sites [19] (Figure 2(a)).

• Incremental graph search algorithms. Many problems in robotics require constant re- planning in response to the discovery of the environment, corrections in the localization of the robot, imperfect actuation and changes in the environment. Jointly with my students and collaborators, we have developed and continue to develop new incremental graph search

(a) (b) (c) (d)

Figure 2: Our work on developing novel graph search algorithms and applying them to planning for autonomous flight and landing, autonomous navigation and control of small teams of robots.

algorithms that speed up repeated planning in such domains by re-using search efforts. Some of these algorithms include D* Lite [18, 16], Real-time Adaptive A* [17], Truncated Incremental Search [1], Anytime Tree-Restoring Weighted A* [14] and Anytime D* [21]. To the best of my knowledge, the algorithm Anytime D* we developed was the first heuristic search to be both anytime and incremental. We have used it to build motion planning for a variety of ground and aerial vehicles including a fully autonomous micro-aerial vehicle (Figure 2(a)) [25], a full-size K-MAX helicopter performing autonomous flight and landing (Figure 2(b)) and a full-size SUV (Figure 2(c)) that won the DARPA Urban Challenge race in 2007 [20].

• Graph search-based planning for mobile manipulation. Much of my inspiration for developing heuristic search algorithms for high-dimensional planning comes from the field of autonomous mobile manipulation. In planning for robotic manipulation, heuristic search-based planning was commonly thought of as impractical due to the high-dimensionality of the plan- ning problem. In the last five years, my group has been developing novel graph search algo- rithms and compact graph representations that, by exploiting some of the properties of mobile manipulation tasks, do achieve real-time performance without sacrificing rigorous guarantees that heuristic search algorithms usually provide [8, 11]. Unlike most other solutions to mo- tion planning for high-DOF mechanisms, these approaches provide deterministic guarantees on completeness and bounds on the sub-optimality of the generated solution with respect to the graph that models the problem. As a result, they typically generate motions that are consistent from run to run, are close to what users anticipate from the robot and minimize cost function well. These approaches have been used for single-arm [10], dual-arm [7], N-arm [9] and full-body manipulation tasks [12] (Figure 1) and run on both academic as well as multiple industrial robotic systems built at CMU (Figure 1(c)) and elsewhere.

Recently, my group began to explore several new directions of research. These directions were motivated by several key observations we made while building search-based planners for physical robots and getting them to run effectively in real-world scenarios. I believe that one of the key benefits we get from transitioning our algorithms onto real robots is making such observations about the characteristics of robotic systems and the tasks they are required to accomplish. These observations enable us to build new classes of algorithms that become capable of solving problems that were previously unsolvable with heuristic search-based planning algorithms. Below I outline several of our recent findings.

• Graph search algorithms that learn to improve their performance based on expe- rience and demonstrations. Robots are often used to perform similar tasks over and over again. It is therefore important for us to study how planning algorithms can improve their speed and robustness based on past planning experiences as well as demonstrations provided by humans and/or other robots. This approach is in contrast to incremental graph search al- gorithms that speed up re-planning within a single execution of a task. To this end, my group has started to research a new class of heuristic searches that are capable of improving their performance based on their previous experiences and demonstrated solutions [27, 28, 2]. For example, we have recently developed a new approach to graph search-based planning that we call Experience Graphs [27]. Planning with Experience Graphs builds a faster-to-solve graph representation of the planning problem based on the solutions it has found previously or demon- strations provided by a person and utilizes this representation to focus the search for a solution in a way that preserves rigorous guarantees on completeness and bounded sub-optimality with respect to the original graph representation of the problem. Planning with Experience Graphs

turned out to be highly beneficial in the variety of complex mobile manipulation tasks ranging from assembly (Figure 1(d)) to paint stripping (Figure 1(c)). To my knowledge, Experience Graphs is the first heuristic search method that “learns from its experience” a more com- pact graph representation that speeds up its future planning times and does it in a way that preserves rigorous guarantees on the solution quality.

• Graph search with multiple heuristics. One of the most recent family of graph search algorithms that my group has developed is Multi-Heuristic A* [4, 30, 15, 3]. These algorithms build on the observation that while in many robotics planning problems it is common to have multiple heuristic functions (i.e., estimates on cost-to-goal) available for guiding the search, it can often be highly ineffective to combine these functions into a single heuristic function that can be utilized by a heuristic search. Furthermore, it is hard to ensure that all of these heuristic functions are admissible and consistent, the properties that are typically required to provide guarantees on completeness and solution quality. Motivated by these observations, we have developed 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 for a solution in a way that guarantees completeness and bounded sub-optimality. This methodology turned out to be highly effective for high- dimensional planning problems such as full-body mobile manipulation that often have several lower-dimensional subspaces that can be used to compute multiple heuristic functions, some of which may be inadmissible. This effectiveness combined with the simplicitly and rigorous theoretical properties of MHA* are typically what I strive to have the most in the algorithms my group and I develop.

In addition to publishing papers on our research, I am eager to see the impact of our results in the real world. To this end, my group has built and actively maintains an open-source library – Search-based Planning Library (SBPL) – that includes many of the graph search algorithms and search-based planning modules that we have developed. This library comes as part of ROS (Robotic Operating System). The SBPL library has been used by a number of universities across the world, various companies and numerous DoD service labs as either a stand-alone library or as part of ROS for such tasks as autonomous navigation, autonomous flight and mobile manipulation. In addition, my group actively participates in transitioning our technology onto fielded systems. Some of the recent examples include developing a motion planner for a full-scale K-MAX helicopter flying at a speed of up to 100 knots (115 mph) and avoiding no-fly zones detected in-flight (Figure 2(b)) and developing manipulation and navigation planners for an industrial mobile manipulator used for paint stripping different aircrafts (Figure 1(c)), the project that won the national Gold Edison award in 2013.

To summarize, I love developing algorithms that are simple, provide strong theoretical guarantees and are effective in solving real-world problems in robotics. In all of my work, I am driven by challenging decision-making problems in robotics. I believe the current state of autonomous robotics is far from mature and the lack of adequate decision-making methods contributes to this. This motivates the work of my group on developing effective decision-making algorithms and showing them in action on physical robots.

References

  1. [1]  S. Aine and M. Likhachev. Truncated incremental search: Faster replanning by exploiting suboptimality. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI), 2013.
  2. [2]  S. Aine, C. Sharma, and M. Likhachev. Learning to search more efficiently from experience: A multi-heuristic approach. In Proceedings of the International Symposium on Combinatorial Search (SoCS), 2015.
  3. [3]  S. Aine, S. Swaminathan, V. Narayanan, V. Hwang, and M. Likhachev. Multi-heuristic A*. International Journal of Robotics Research (IJRR). Accepted for publication.
  4. [4]  S. Aine, S. Swaminathan, V. Narayanan, V. Hwang, and M. Likhachev. Multi-heuristic A*. In Proceedings of the Robotics: Science and Systems Conference (RSS), 2014.
  5. [5]  S. Bhattacharya, V. Kumar, and M. Likhachev. Distributed optimization with pairwise constraints and its ap- plication to multi-robot path planning. In Proceedings of the Robotics: Science and Systems Conference (RSS), 2010.
  6. [6]  S. Chitta, B. Cohen, and M. Likhachev. Planning for autonomous door opening with a mobile manipulator. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2010.
  7. [7]  B.Cohen,S.Chitta,andM.Likhachev.Search-basedplanningfordual-armmanipulationwithuprightorientation constraints. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2012.
  8. [8]  B. Cohen, S. Chitta, and M. Likhachev. Heuristic search-based planning for manipulation. International Journal of Robotics Research (IJRR), 2013.
  9. [9]  B. Cohen, M. Phillips, and M. Likhachev. Planning single-arm manipulations with n-arm robots. In Proceedings of Robotics: Science and Systems (RSS), 2014.
  10. [10]  B. Cohen, G. Subramanian, S. Chitta, and M. Likhachev. Planning for manipulation with adaptive motion primitives. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2011.
  11. [11]  K. Gochev, B. Cohen, J. Butzke, A. Safonova, and M. Likhachev. Path planning with adaptive dimensionality. In Proceedings of the International Symposium on Combinatorial Search (SoCS), 2011.
  12. [12]  K. Gochev, A. Safonova, and M. Likhachev. Planning with adaptive dimensionality for mobile manipulation. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2012.
  13. [13]  K. Gochev, A. Safonova, and M. Likhachev. Incremental planning with adaptive dimensionality. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2013.
  14. [14]  K. Gochev, A. Safonova, and M. Likhachev. Anytime tree-restoring weighted A* graph search. In Proceedings of the International Symposium on Combinatorial Search (SoCS), 2014.
  15. [15]  F. Islam, V. Narayanan, and M. Likhachev. Dynamic multi-heuristic A*. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2015.
  16. [16]  S. Koenig and M. Likhachev. D* Lite. In Proceedings of the Eighteenth National Conference on Artificial Intelli- gence (AAAI), 2002.
  17. [17]  S. Koenig and M. Likhachev. Real-time adaptive A*. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2006.
  18. [18]  S. Koenig, M. Likhachev, and D. Furcy. Lifelong planning A*. Artificial Intelligence Journal (AIJ), 2004.
  19. [19]  A. Kushleyev, B. MacAllister, and M. Likhachev. Planning for landing site selection in the aerial supply delivery.In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2011.
  20. [20]  M. Likhachev and D. Ferguson. Planning long dynamically-feasible maneuvers for autonomous vehicles. Interna-tional Journal of Robotics Research (IJRR), 2009.
  21. [21]  M. Likhachev, D. Ferguson, G. Gordon, A. Stentz, and S. Thrun. Anytime search in dynamic graphs. ArtificialIntelligence Journal (AIJ), 172(14), 2008.
  22. [22]  M. Likhachev, G. Gordon, and S. Thrun. ARA*: Anytime A* with provable bounds on sub-optimality. InAdvances in Neural Information Processing Systems (NIPS) 16. Cambridge, MA: MIT Press, 2003.
  23. [23]  M. Likhachev and A. Stentz. R* search. In Proceedings of the National Conference on Artificial Intelligence(AAAI), 2008.
  24. [24]  M. Likhachev and A. Stentz. Probabilistic planning with clear preferences on missing information. Artificial Intelligence Journal (AIJ), 173(5-6):696–721, 2009.
  1. [25]  B. MacAllister, A. Kushleyev, J. Butzke, and M. Likhachev. Path planning for non-circular micro aerial vehicles in constrained environments. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2013.
  2. [26]  V. Narayanan, M. Phillips, and M. Likhachev. Anytime safe interval path planning for dynamic environments. In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2012.
  3. [27]  M. Phillips, B. Cohen, S. Chitta, and M. Likhachev. E-graphs: Bootstrapping planning with experience graphs. In Proceedings of the Robotics: Science and Systems Conference (RSS), 2012.
  4. [28]  M. Phillips, V. Hwang, S. Chitta, and M. Likhachev. Learning to plan for constrained manipulation from demon- strations. Autonomous Robots (AURO). Accepted for publication.
  5. [29]  M. Phillips and M. Likhachev. Planning in domains with cost function dependent actions. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 2011.
  6. [30]  M. Phillips, V. Narayanan, S. Aine, and M. Likhachev. Efficient search with an ensemble of heuristics. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2015.
Displaying 136 Publications
Search-based Planning Library (SBPL) including a collection of graph searches and planners that utilize these graph searches
Maxim Likhachev

Miscellaneous, software - open-source and is available under http://www.sbpl.net (under software tab), November, 2018
A Single-Planner Approach to Multi-Modal Humanoid Mobility
Andrew Dornbush, Karthik Vijayakumar, Sameer Bardapurkar, Fahad Islam and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018), May, 2018
Effective Footstep Planning for Humanoids Using Homotopy-Class Guidance
Vinitha Ranganeni, Oren Salzman and Maxim Likhachev

Conference Paper, International Conference on Automated Planning and Scheduling, January, 2018
Towards Adaptability of Demonstration-Based Training of NPC Behavior
John Drake, Alla Safonova and Maxim Likhachev

Conference Paper, In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2017), October, 2017
Heuristic Search on Graphs with Existence Priors for Expensive-to-Evaluate Edges
Venkatraman Narayanan and Maxim Likhachev

Conference Paper, International Conference on Automated Planning and Scheduling (ICAPS), June, 2017
Learning to Avoid Local Minima in Planning for Static Environments
Shivam Vats, Venkatraman Narayanan and Maxim Likhachev

Conference Paper, International Conference on Automated Planning and Scheduling (ICAPS), June, 2017
Parts Assembly Planning under Uncertainty with Simulation-Aided Physical Reasoning
Sung Kyun Kim and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA 2017), pp. 4074-4081, May, 2017
Deliberative Object Pose Estimation in Clutter
Venkatraman Narayanan and Maxim Likhachev

Conference Paper, 2017 IEEE International Conference on Robotics and Automation (ICRA), May, 2017
Demonstration-Based Training of Non-Player Character Tactical Behaviors
John Drake, Alla Safonova and Maxim Likhachev

Conference Paper, In Proceedings of the Twelfth Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2016), October, 2016
Robot Planning in the Real World: Research Challenges and Opportunities
Ron Alterovitz, Sven Koenig and Maxim Likhachev

Magazine Article, Artificial Intelligence Magazine, Vol. 37, No. 2, pp. 76 - 84, July, 2016
Search Portfolio with Sharing
Sandip Aine and Maxim Likhachev

Conference Paper, In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2016), pp. 11 - 19, June, 2016
Truncated Incremental Search: Faster Replanning by Exploiting Suboptimality
Sandip Aine and Maxim Likhachev

Journal Article, Artificial Intelligence, Vol. 234, pp. 49 - 77, May, 2016
Planning for a Ground-Air Robotic System with Collaborative Localization
Jonathan Butzke, Kalin Gochev, Benjamin Holden, Eui-Jung Jung and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2016), May, 2016
A*-Connect: Bounded Suboptimal Bidirectional Heuristic Search
Fahad Islam, Venkatraman Narayanan and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2016
PERCH: Perception via Search for Multi-Object Recognition and Localization
Venkatraman Narayanan and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2016
Planning for Grasp Selection of Partially Occluded Objects
Sung Kyun Kim and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2016
Learning to Plan for Constrained Manipulation from Demonstrations
Mike Phillips, Victor Hwang, Sachin Chitta and Maxim Likhachev

Journal Article, Autonomous Robots (AURO), Vol. 40, No. 1, pp. 109 – 124, January, 2016
Multi-Heuristic A*
Sandip Aine, Siddharth Swaminathan, Venkatraman Narayanan, Victor Hwang and Maxim Likhachev

Journal Article, International Journal of Robotics Research (IJRR), January, 2016
3-D Exploration with an Air-Ground Robotic System
Jonathan Butzke, Andrew Dornbush and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), September, 2015
Path Planning for a Tethered Robot Using Multi-Heuristic A* with Topology-Based Heuristics
Soonkyum Kim and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), September, 2015
Efficient Search with an Ensemble of Heuristics
Michael Phillips, Venkatraman Narayanan, Sandip Aine and Maxim Likhachev

Conference Paper, International Joint Conference on Artificial Intelligence (IJCAI), July, 2015
Improved Multi-Heuristic A* for Searching with Uncalibrated Heuristics
Venkatraman Narayanan, Sandip Aine and Maxim Likhachev

Conference Paper, International Symposium on Combinatorial Search (SoCS), June, 2015
Learning to Search More Efficiently from Experience: A Multi-heuristic Approach
Sandip Aine, Charupriya Sharma and Maxim Likhachev

Conference Paper, In Proceedings of the International Symposium on Combinatorial Search (SoCS 2015), June, 2015
A Web-based Infrastructure for Recording User Demonstrations of Mobile Manipulation Tasks
Ellis Ratner, Benjamin Cohen, Mike Phillips and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2015), May, 2015
Lazy Validation of Experience Graphs
Victor Hwang, Mike Phillips, Siddhartha Srinivasa and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2015), May, 2015
Planning for multi-agent teams with leader switching
Siddharth Swaminathan, Mike Phillips and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2015), May, 2015
Speeding up heuristic computation in planning with Experience Graphs
Mike Phillips and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2015), May, 2015
Dynamic Multi-Heuristic A*
Fahad Islam, Venkatraman Narayanan and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2015
Task-Oriented Planning for Manipulating Articulated Mechanisms Under Model Uncertainty
Venkatraman Narayanan and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2015
CHIMP, the CMU Highly Intelligent Mobile Platform
Anthony (Tony) Stentz, Herman Herman, Alonzo Kelly, Eric Meyhofer, Galen Clark Haynes, David Stager, Brian Zajac, J. Andrew (Drew) Bagnell, Jordan Brindza, Christopher Dellin, Michael George, Jose Gonzalez-Mora, Sean Hyde, Morgan Jones, Michel Laverne, Maxim Likhachev, Levi Lister, Matthew D. Powers, Oscar Ramos, Justin Ray, David P. Rice, Justin Scheifflee, Raumi Sidki, Siddhartha Srinivasa, Kyle Strabala, Jean Philippe Tardif, Jean-Sebastien Valois, J Michael Vandeweghe, Michael D. Wagner and Carl Wellington

Journal Article, Carnegie Mellon University, Journal of Field Robotics (JFR), Special Issue: Special issue on DARPA Robotics Challenge (DRC), Vol. 32, No. 2, pp. 209-228, March, 2015
Robotic Handwriting: Multi-Contact Manipulation Based on Reactional Internal Contact Hypothesis
Sung-Kyun Kim, Joonhee Jo, Yonghwan Oh, Sang-Rok Oh, Siddhartha Srinivasa and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), September, 2014
State Lattice with Controllers: Augmenting Lattice-Based Path Planning with Controller-Based Motion Primitives
Jonathan Butzke, Krishna Sapkota, Kush Prasad, Brian MacAllister and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September, 2014
Robotic Handwriting: Multi-contact Manipulation based on Reactional Internal Contact Hypothesis
Sung Kyun Kim, Joonhee Jo, Yonghwan Oh, Sang-Rok Oh, Siddhartha Srinivasa and Maxim Likhachev

Conference Paper, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September, 2014
Prioritized Computation for Numerical Sound Propagation
John Drake, Maxim Likhachev and Alla Safonova

Conference Paper, In Proceedings of the 17th International Conference on Digital Audio Effects (DAFx), August, 2014
Anytime Tree-Restoring Weighted A* Graph Search
Kalin Gochev, Alla Safonova and Maxim Likhachev

Conference Paper, In Proceedings of the International Symposium on Combinatorial Search (SoCS 2014), August, 2014
Multi-Heuristic A*
Sandip Aine, Siddharth Swaminathan, Venkatraman Narayanan, Victor Hwang and Maxim Likhachev

Conference Paper, Robotics: Science and Systems, July, 2014
Planning Single-arm Manipulations with n-Arm Robots
Benjamin Cohen, Mike Phillips and Maxim Likhachev

Conference Paper, In Proceedings of the Robotics: Science and Systems Conference (RSS 2014), July, 2014
Parallel A* for Planning with Time-consuming State Expansions
Mike Phillips, Sven Koenig and Maxim Likhachev

Conference Paper, In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2014), June, 2014
Coordinated Commencement of Pre-Planned Routes for Fixed-Wing UAS Starting from Arbitrary Locations – a Near Real-Time Solution
James Keller, Dinesh Thakur, Vladimir Dobrokhodov, Kevin Jones, Maxim Likhachev, Jean Gallier, Isaac Kaminer and Vijay Kumar

Conference Paper, In Proceedings of International Conference on Unmanned Aircraft Systems, (ICUAS 2014), May, 2014
Motion Planning for Smooth Pickup of Moving Objects
Arjun Menon, Benjamin Cohen and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2014), May, 2014
Motion Planning for Robotic Manipulators with Independent Wrist Joints
Kalin V. Gochev, Venkatraman Narayanan, Benjamin Cohen, Alla Safonova and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2014
Stochastic Activity Authoring with Direct User Control
Aline Normoyle, Maxim Likhachev and Alla Safonova

Conference Paper, In Proceedings of the 18th meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D '14), pp. 31 - 38, March, 2014
Single- and Dual-Arm Motion Planning with Heuristic Search
Benjamin Cohen, Sachin Chitta and Maxim Likhachev

Journal Article, International Journal of Robotics Research (IJRR), Vol. 33, No. 3, pp. 305 - 320, February, 2014
Perception and Motion Planning for Pick-and-Place of Dynamic Objects
Anthony Cowley, Benjamin Cohen, William Marshall, Camillo J. Taylor and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), November, 2013
Planning for Opportunistic Surveillance with Multiple Robots
Dinesh Thakur, Maxim Likhachev, James Keller, Vijay Kumar, Vladimir Dobrokhodov, Kevin Jones, Jeff Wurz and Isaac Kaminer

Conference Paper, In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), November, 2013
Anytime Truncated D*: Anytime Replanning with Truncation
Sandip Aine and Maxim Likhachev

Conference Paper, In Proceedings of the International Symposium on Combinatorial Search (SoCS 2013), July, 2013
Truncated Incremental Search: Faster Replanning by Exploiting Suboptimality
Sandip Aine and Maxim Likhachev

Conference Paper, In Proceedings of the National Conference on Artificial Intelligence (AAAI 2013), July, 2013
Learning to Plan for Constrained Manipulation from Demonstrations
Mike Phillips, Victor Hwang, Sachin Chitta and Maxim Likhachev

Conference Paper, In Proceedings of the Robotics: Science and Systems Conference (RSS 2013), June, 2013
Incremental Planning with Adaptive Dimensionality
Kalin Gochev, Alla Safonova and Maxim Likhachev

Conference Paper, In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2013), June, 2013
A Single Planner for a Composite Task of Approaching, Opening and Navigating through Non-spring and Spring-loaded Doors
Steven Gray, Sachin Chitta, Vijay Kumar and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2013), May, 2013
Anytime Incremental Planning with E-Graphs
Mike Phillips, Andrew Dornbush, Sachin Chitta and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2013), May, 2013
Path Planning for Non-Circular Micro Aerial Vehicles in Constrained Environments
Brian MacAllister, Jonathan Butzke, Aleksandr Kushleyev and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2013), May, 2013
Planning Under Topological Constraints Using Beam Graphs
Venkatraman Narayanan, Paul Vernaza, Maxim Likhachev and Steven M. LaValle

Conference Paper, 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 431-437, May, 2013
Planning with Approximate Preferences and its Application to Disambiguating Human Intentions in Navigation
Bradford Neuman and Maxim Likhachev

Conference Paper, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2013), May, 2013
Experience Graph-based Planning with Learning
Maxim Likhachev

Miscellaneous, software - open-source and is available under http://www.sbpl.net (under software tab), December, 2012
Planner for dual-arm manipulation by PR2 (ROS package)
Maxim Likhachev and Michael Kaess

Miscellaneous, software - open-source and is available under http://www.sbpl.net (under software tab), December, 2012
Anytime Footstep Planning with Suboptimality Bounds
Armin Hornung, Andrew Dornbush, Maxim Likhachev and Maren Bennewitz

Conference Paper, In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS 2012), November, 2012
Topological Constraints in Search-based Robot Path Planning
Subhrajit Bhattacharya, Maxim Likhachev and Vijay Kumar

Journal Article, Autonomous Robots (AURO), Vol. 33, No. 3, pp. 273 – 290, October, 2012
Anytime Safe Interval Path Planning for Dynamic Environments
Venkatraman Narayanan, Michael Phillips and Maxim Likhachev

Conference Paper, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 2012
Game-based Data Capture for Player Metrics
Aline Normoyle, John Drake, Maxim Likhachev and Alla Safonova

Conference Paper, In Proceedings of the Eighth Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-12), October, 2012
The University of Pennsylvania MAGIC 2010 multi-robot unmanned vehicle system
Jon Butzke, Kostas Daniilidis, Alex Kushleyev, Dan D. Lee, Maxim Likhachev, Cody Phillips and Mike Phillips

Journal Article, Journal of Field Robotics (JFR), Vol. 29, No. 5, pp. 745 - 761, September, 2012
Heuristic search comes of age
Nathan R. Sturtevant, Ariel Felner, Maxim Likhachev and Wheeler Ruml

Conference Paper, In Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (AAAI 2012), July, 2012
Search-based Path Planning with Homotopy Class Constraints in 3D
Subhrajit Bhattacharya, Maxim Likhachev and Vijay Kumar

Conference Paper, In Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (AAAI 2012), Invited Paper, July, 2012
E-Graphs: Bootstrapping Planning with Experience Graphs
Mike Phillips, Benjamin Cohen, Sachin Chitta and Maxim Likhachev

Conference Paper, In Proceedings of the Robotics: Science and Systems Conference (RSS 2012), July, 2012
Efficiently finding optimal winding-constrained loops in the plane
Paul Vernaza, Venkatraman Narayanan and Maxim Likhachev

Conference Paper, Proceedings of Robotics: Science and Systems, July, 2012
Combining Global and Local Planning with Guarantees on Completeness
Haojie Zhang, Jon Butzke, and Maxim Likhachev

Conference Paper, roceedings of the IEEE International Conference on Robotics and Automation (ICRA 2012), May, 2012
Navigation in Three-Dimensional Cluttered Environments for Mobile Manipulation
Armin Hornung, Mike Phillips, Edward Gil Jones, Maren Bennewitz, Maxim Likhachev and Sachin Chitta

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2012), May, 2012
Planning with Adaptive Dimensionality for Mobile Manipulation
Kalin Gochev, Alla Safonova and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2012), May, 2012
Search-Based Planning for Dual-Arm Manipulation with Upright Orientation Constraints
Benjamin Cohen, Sachin Chitta and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2012), May, 2012
Using State Dominance for Path Planning in Dynamic Environments with Moving Obstacles
Juan Pablo Gonzalez, Andrew Dornbush and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2012), May, 2012
Planner for 3D navigation with dynamics constraints (ROS package)
Maxim Likhachev

Miscellaneous, software - open-source and is available under http://www.sbpl.net (under software tab), December, 2011
Multi-hypothesis Motion Planning for Visual Object Tracking
Haifeng Gong, Jack Sim, Maxim Likhachev and Jianbo Shi

Conference Paper, International Conference on Computer Vision (ICCV 2011), November, 2011
Planning for Landing Site Selection in the Aerial Supply Delivery
Aleksandr Kushleyev, Brian MacAllister and Maxim Likhachev

Conference Paper, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), September, 2011
Planning for Multi-Robot Exploration With Multiple Objective Utility Functions
Jonathan Butzke and Maxim Likhachev

Conference Paper, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), September, 2011
Path Planning with Adaptive Dimensionality
Kalin Gochev, Benjamin Cohen, Jonathan Butzke, Alla Safonova and Maxim Likhachev

Conference Paper, Proceedings of the International Symposium on Combinatorial Search (SoCS 2011), July, 2011
Planning in Domains with Cost Function Dependent Actions
Mike Phillips and Maxim Likhachev

Conference Paper, Proceedings of the National Conference on Artificial Intelligence (AAAI 2011), July, 2011
Search-Based Planning with Provable Suboptimality Bounds for Continuous State Spaces
Juan Pablo Gonzalez and Maxim Likhachev

Conference Paper, Proceedings of the International Symposium on Combinatorial Search (SoCS 2011), July, 2011
Identification and Representation of Homotopy Classes of Trajectories for Search-based Path Planning in 3D
Subhrajit Bhattacharya, Maxim Likhachev and Vijay Kumar

Conference Paper, Proceedings of the Robotics: Science and Systems Conference (RSS 2011), (Best Paper Award), June, 2011
Cart Pushing with a Mobile Manipulation System: Towards Navigation with Moveable Objects
Jonathan Scholz, Sachin Chitta, Bhaskara Marthi and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011), May, 2011
Planning for Manipulation with Adaptive Motion Primitives
Benjamin Cohen, Gokul Subramanian, Sachin Chitta and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011), May, 2011
SIPP: Safe Interval Path Planning for Dynamic Environments
Mike Phillips and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011), May, 2011
Planner for single-arm manipulation (ROS package)
Maxim Likhachev

Miscellaneous, software - open-source and is available under http://www.sbpl.net (under software tab), December, 2010
Search-based Path Planning with Homotopy Class Constraints
Subhrajit Bhattacharya, Vijay Kumar and Maxim Likhachev

Conference Paper, Proceedings of the National Conference on Artificial Intelligence (AAAI 2010), July, 2010
Distributed Optimization with Pairwise Constraints and its Application to Multi-robot Path Planning
Subhrajit Bhattacharya, Vijay Kumar and Maxim Likhachev

Conference Paper, Proceedings of the Robotics: Science and Systems Conference (RSS 2010), June, 2010
High-dimensional Planning on the GPU
Joseph T. Kider Jr., Mark Henderson, Maxim Likhachev and Alla Safonova

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2010), May, 2010
Multi-agent Path Planning with Multiple Tasks and Distance Constraints
Subhrajit Bhattacharya, Maxim Likhachev and Vijay Kumar

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2010), May, 2010
Planning for Autonomous Door Opening with a Mobile Manipulator
Sachin Chitta, Benjamin Cohen and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2010), May, 2010
Search-based Planning for Manipulation with Motion Primitives
Benjamin Cohen, Sachin Chitta and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2010), May, 2010
High-Dimensional Planning on the GPU
Mark Henderson, Joseph T. Kider Jr., Maxim Likhachev and Alla Safonova

Miscellaneous, NVIDIA GPU Technology Conference (Best Poster Award), November, 2009
Efficient Cost Computation in Cost Map Planning for Non-Circular Robots
Jennifer King and Maxim Likhachev

Conference Paper, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), October, 2009
Planning Long Dynamically-Feasible Maneuvers for Autonomous Vehicles
Maxim Likhachev and Dave Ferguson

Journal Article, The International Journal of Robotics Research (IJRR), Vol. 28, No. 8, pp. 933 - 945, August, 2009
Incremental Phi*: Incremental Any-Angle Path Planning on Grids
Alex Nash, Sven Koenig and Maxim Likhachev

Conference Paper, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2009), July, 2009
Path Clearance
Maxim Likhachev and Anthony Stentz

Journal Article, IEEE Robotics and Automation Magazine (RAM), Special Issue on Cooperative Control of Multiple Heterogeneous Unmanned Aerial Vehicles for Coverage and Surveillance, Vol. 15, No. 2, pp. 62 - 72, June, 2009
Search-based Planning for a Legged Robot over Rough Terrain
Paul Vernaza, Maxim Likhachev, Subhrajit Bhattacharya, Sachin Chitta, Aleksandr Kushleyev and Daniel D. Lee

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2009), May, 2009
Time-bounded Lattice for Efficient Planning in Dynamic Environments
Aleksandr Kushleyev and Maxim Likhachev

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2009), May, 2009
Probabilistic Planning with Clear Preferences on Missing Information
Maxim Likhachev and Anthony Stentz

Journal Article, Artificial Intelligence Journal (AIJ), Vol. 173, No. 5, pp. 696 - 721, April, 2009
Planner for 3D navigation with full-body collision checking (ROS package)
Maxim Likhachev

Miscellaneous, software - open-source and is available under http://www.sbpl.net (under software tab), December, 2008
Anytime Search in Dynamic Graphs
Maxim Likhachev, Dave Ferguson, Geoff Gordon, Anthony Stentz and Sebastian Thrun

Journal Article, Artificial Intelligence Journal (AIJ), Vol. 172, No. 14, pp. 1613 - 1643, September, 2008
Motion Planning in Urban Environments: Part I
David Ferguson, Thomas Howard and Maxim Likhachev

Conference Paper, Proceedings of the IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems, September, 2008
Motion Planning in Urban Environments: Part II
David Ferguson, Thomas Howard and Maxim Likhachev

Conference Paper, Proceedings of the IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems, September, 2008
R* Search
Maxim Likhachev and Anthony Stentz

Conference Paper, Proceedings of the National Conference on Artificial Intelligence (AAAI 2008), July, 2008
Planning Long Dynamically-Feasible Maneuvers for Autonomous Vehicles
Maxim Likhachev and Dave Ferguson

Conference Paper, Proceedings of the Robotics: Science and Systems Conference (RSS 2008), June, 2008
A Reasoning Framework for Autonomous Urban Driving
David Ferguson, Christopher R. Baker, Maxim Likhachev and John M. Dolan

Conference Paper, Proceedings of the IEEE Intelligent Vehicles Symposium (IV 2008), pp. 775-780, June, 2008
Autonomous driving in urban environments: Boss and the Urban Challenge
Christopher Urmson, Joshua Anhalt, Hong Bae, J. Andrew (Drew) Bagnell, Christopher R. Baker, Robert E. Bittner, Thomas Brown, M. N. Clark, Michael Darms, Daniel Demitrish, John M. Dolan, David Duggins, David Ferguson, Tugrul Galatali, Christopher M. Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas Howard, Sascha Kolski, Maxim Likhachev, Bakhtiar Litkouhi, Alonzo Kelly, Matthew McNaughton, Nick Miller, Jim Nickolaou, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul Rybski, Varsha Sadekar, Bryan Salesky, Young-Woo Seo, Sanjiv Singh, Jarrod M. Snider, Joshua C. Struble, Anthony (Tony) Stentz, Michael Taylor, William (Red) L. Whittaker, Ziv Wolkowicki, Wende Zhang and Jason Ziglar

Journal Article, Carnegie Mellon University, Journal of Field Robotics Special Issue on the 2007 DARPA Urban Challenge, Part I, Vol. 25, No. 8, pp. 425-466, June, 2008
Efficiently Using Cost Maps For Planning Complex Maneuvers
Dave Ferguson and Maxim Likhachev

Conference Paper, In Proceedings of International Conference on Robotics and Automation Workshop on Planning with Cost Maps, May, 2008
Information Value-Driven Approach to Path Clearance with Multiple Scout Robots
Maxim Likhachev and Anthony Stentz

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2008), May, 2008
Goal Directed Navigation with Uncertainty in Adversary Locations
Maxim Likhachev and Anthony Stentz

Conference Paper, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), October, 2007
Speeding up Moving-Target Search
Sven Koenig, Maxim Likhachev and Xiaoxun Sun

Conference Paper, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), May, 2007
Tartan Racing: A Multi-Modal Approach to the DARPA Urban Challenge
Christopher Urmson, Joshua Anhalt, J. Andrew (Drew) Bagnell, Christopher R. Baker, Robert E. Bittner, John M. Dolan, David Duggins, David Ferguson, Tugrul Galatali, Hartmut Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas Howard, Alonzo Kelly, David Kohanbash, Maxim Likhachev, Nick Miller, Kevin Peterson, Raj Rajkumar, Paul Rybski, Bryan Salesky, Sebastian Scherer, Young-Woo Seo, Reid Simmons, Sanjiv Singh, Jarrod M. Snider, Anthony (Tony) Stentz, William (Red) L. Whittaker and Jason Ziglar

Tech. Report, Robotics Institute, Carnegie Mellon University, DARPA Grand Challenge Tech Report, April, 2007
Path Clearance Using Multiple Scout Robots
Maxim Likhachev and Anthony Stentz

Conference Paper, Proceedings of the Army Science Conference (ASC 2006), November, 2006
Probabilistic networks for detecting signal content
Maxim Likhachev and Murat Eren

Miscellaneous, Patent 7,136,813 (issued 11/14/2006, filed 09/25/2005), Current Assignee Micron Technology Inc., Original Assignee Intel Corp., November, 2006
PPCP: Efficient Probabilistic Planning with Clear Preferences in Partially-Known Environments
Maxim Likhachev and Anthony Stentz

Conference Paper, Proceedings of the National Conference on Artificial Intelligence (AAAI 2006), July, 2006
Incremental Heuristic Search in Games: The Quest for Speed
Maxim Likhachev and Sven Koenig

Miscellaneous, In Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2006), Poster Abstract, June, 2006
Real-Time Adaptive A*
Sven Koenig and Maxim Likhachev

Conference Paper, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), May, 2006
Planning for Markov Decision Processes with Sparse Stochasticity
Maxim Likhachev, Geoff Gordon and Sebastian Thrun

Conference Paper, Advances in Neural Information Processing Systems (NIPS 2005), December, 2005
Bounded Real-Time Dynamic Programming: RTDP with monotone upper bounds and performance guarantees
H. Brendan McMahan, Maxim Likhachev and Geoff Gordon

Conference Paper, Proceedings of the International Conference on Machine Learning (ICML 2005), August, 2005
Adaptive A*
Sven Koenig and Maxim Likhachev

Miscellaneous, In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), Poster Abstract, July, 2005
A Generalized Framework for Lifelong Planning A*
Maxim Likhachev and Sven Koenig

Conference Paper, Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2005), June, 2005
A Guide to Heuristic-based Path Planning
David Ferguson, Maxim Likhachev and Anthony (Tony) Stentz

Conference Paper, Proceedings of the International Workshop on Planning under Uncertainty for Autonomous Systems, International Conference on Automated Planning and Scheduling (ICAPS), June, 2005
A New Principle for Incremental Heuristic Search: Theoretical Results
Sven Koenig and Maxim Likhachev

Miscellaneous, In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2005), Poster Abstract, June, 2005
Anytime Dynamic A*: An Anytime, Replanning Algorithm
Maxim Likhachev, David Ferguson, Geoffrey Gordon, Anthony (Tony) Stentz and Sebastian Thrun

Conference Paper, Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), June, 2005
Fast Replanning for Navigation in Unknown Terrain
Sven Koenig and Maxim Likhachev

Journal Article, IEEE Transactions on Robotics, Vol. 21, No. 3, pp. 354 - 363, June, 2005
Anytime Dynamic A*: The Proofs
Maxim Likhachev, David Ferguson, Geoffrey Gordon, Anthony (Tony) Stentz and Sebastian Thrun

Tech. Report, CMU-RI-TR-05-12, Robotics Institute, Carnegie Mellon University, May, 2005
Incremental Heuristic Search in Artificial Intelligence
Sven Koenig, Maxim Likhachev, Yaxin Liu and David Furcy

Magazine Article, Artificial Intelligence Magazine, Vol. 25, No. 2, pp. 99 - 112, June, 2004
Lifelong Planning A*
Sven Koenig, Maxim Likhachev and David Furcy

Journal Article, Artificial Intelligence Journal (AIJ), Vol. 155, No. 1, pp. 93 - 146, May, 2004
ARA*: Anytime A* with Provable Bounds on Sub-Optimality
Maxim Likhachev, Geoff Gordon and Sebastian Thrun

Conference Paper, Advances in Neural Information Processing Systems (NIPS 2003), December, 2003
Speeding up the Parti-Game Algorithm
Maxim Likhachev and Sven Koenig

Conference Paper, Advances in Neural Information Processing Systems (NIPS 2003), December, 2003
Lifelong Planning for Mobile Robots
Maxim Likhachev and Sven Koenig

Book Section/Chapter, Lecture Notes in Artificial Intelligence, Advances in Plan-Based Control of Robotic Agents, Vol. 2466, pp. 140-156, November, 2002
Incremental Replanning for Mapping
Maxim Likhachev and Sven Koenig

Conference Paper, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), September, 2002
D* Lite
Sven Koenig and Maxim Likhachev

Conference Paper, Proceedings of the National Conference on Artificial Intelligence (AAAI 2002), July, 2002
Learning Behavioral Parameterization Using Spatio-Temporal Case-Based Reasoning
Maxim Likhachev, Michael Kaess and Ronald C. Arkin

Conference Paper, IEEE Intl. Conf. on Robotics and Automation, ICRA, pp. 1282-1289, May, 2002
Selection of Behavioral Parameters: Integration of Discontinuous Switching via Case-Based Reasoning with Continuous Adaptation via Learning Momentum
J. Brian Lee, Maxim Likhachev and Ronald C. Arkin

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2002), May, 2002
Incremental A*
Sven Koenig and Maxim Likhachev

Conference Paper, Advances in Neural Information Processing Systems (NIPS 2001), December, 2001
Spatio-Temporal Case-Based Reasoning for Behavioral Selection
Maxim Likhachev and Ronald C. Arkin

Conference Paper, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2001), May, 2001
Robotic Comfort Zones
Maxim Likhachev and Ronald C. Arkin

Conference Paper, Proceedings of SPIE: Sensor Fusion and Decentralized Control in Robotic Systems III Conference, Vol. 4196, pp. 27 - 41, October, 2000
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