Robotics
Where hardware, software and machine intelligence come together.
Our research is interdisciplinary and focuses on sensing, planning, reasoning, and control of single and multi-agent systems, including both manipulation and mobile robots. We strive to develop algorithms and methods for factory automation, smart building and transportation applications using machine learning, computer vision, RF/optical sensing, wireless communications, control theory and signal processing. Key research themes include bin picking and object manipulation, sensing and mapping of indoor areas, coordinated control of robot swarms, as well as robot learning and simulation.
Quick Links
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Researchers
Devesh K.
Jha
Diego
Romeres
Daniel N.
Nikovski
Arvind
Raghunathan
Stefano
Di Cairano
Yebin
Wang
Siddarth
Jain
William S.
Yerazunis
Mouhacine
Benosman
Toshiaki
Koike-Akino
Karl
Berntorp
Radu
Corcodel
Tim K.
Marks
Scott A.
Bortoff
Kei
Ota
Rien
Quirynen
Ye
Wang
Avishai
Weiss
Matthew
Brand
Marcus
Greiff
Abraham P.
Vinod
Bingnan
Wang
Anoop
Cherian
Jianlin
Guo
Jonathan
Le Roux
Hassan
Mansour
Marcel
Menner
Philip V.
Orlik
Koon Hoo
Teo
Anthony
Vetro
Pedro
Miraldo
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Awards
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AWARD MERL Researchers Win Best Workshop Poster Award at the 2023 IEEE International Conference on Robotics and Automation (ICRA) Date: June 2, 2023
Awarded to: Yuki Shirai, Devesh Jha, Arvind Raghunathan and Dennis Hong
MERL Contacts: Devesh K. Jha; Arvind Raghunathan
Research Areas: Artificial Intelligence, Optimization, RoboticsBrief- MERL's paper titled: "Closed-Loop Tactile Controller for Tool Manipulation" Won the Best Poster Award in the workshop on "Embracing contacts : Making robots physically interact with our world". First author and MERL intern, Yuki Shirai, was presented with the award at a ceremony held at ICRA in London. MERL researchers Devesh Jha, Principal Research Scientist, and Arvind Raghunathan, Senior Principal Research Scientist and Senior Team Leader as well as Prof. Dennis Hong of University of California, Los Angeles are also coauthors.
The paper presents a technique to manipulate an object using a tool in a closed-loop fashion using vision-based tactile sensors. More information about the workshop and the various speakers can be found here https://sites.google.com/view/icra2023embracingcontacts/home.
- MERL's paper titled: "Closed-Loop Tactile Controller for Tool Manipulation" Won the Best Poster Award in the workshop on "Embracing contacts : Making robots physically interact with our world". First author and MERL intern, Yuki Shirai, was presented with the award at a ceremony held at ICRA in London. MERL researchers Devesh Jha, Principal Research Scientist, and Arvind Raghunathan, Senior Principal Research Scientist and Senior Team Leader as well as Prof. Dennis Hong of University of California, Los Angeles are also coauthors.
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AWARD Marcus Greiff receives Outstanding Student Paper Award at CCTA 2022 Date: August 25, 2022
Awarded to: Marcus Greiff
MERL Contact: Marcus Greiff
Research Areas: Control, Dynamical Systems, RoboticsBrief- Marcus Greiff, a Visiting Research Scientist at MERL, was awarded one of three outstanding student paper awards at the IEEE CCTA 2022 conference for his paper titled "Quadrotor Control on SU(2)xR3 with SLAM Integration". The award was given for originality, clarity, and potential impact on practical applications of control. The work presents a complete UAV control system design, facilitating autonomous supermarket inventorying without the need for external motion capture systems. A video of the experiments is on YouTube, including both simulations and real-time examples.
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AWARD Best student paper award at RSS22 Date: June 29, 2022
Awarded to: Weizhe Chen
MERL Contact: Diego Romeres
Research Area: RoboticsBrief- Weizhe Chen, a current intern at MERL from Indiana University, Bloomington, Indiana, USA, won the best student paper award at the Robotics Science and Systems (RSS) 2022 conference. The research at Weizhe Chen's university leading up to the awarded paper titled 'AK: Attentive Kernel for Information Gathering', proposes a novel non stationary kernel called, Attentive Kernel, for Gaussian Process Regression. The novel kernel is used to guide a planner to accumulate more valuable data in an elevation mapping task.
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News & Events
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NEWS Ankush Chakrabarty co-organized three sessions at the ACC2023, and was nominated for Best Energy Systems Paper. Date: June 30, 2023 - June 2, 2023
Where: San Diego, CA
MERL Contact: Ankush Chakrabarty
Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- Ankush Chakrabarty (researcher, Multiphysical Systems Team) co-organized and spoke at 3 sessions at the 2023 American Control Conference in San Diego, CA. These include: (1) A tutorial session (w/ Stefano Di Cairano) on "Physics Informed Machine Learning for Modeling and Control": an effort with contributions from multiple academic institutes and US research labs; (2) An invited session on "Energy Efficiency in Smart Buildings and Cities" in which his paper (w/ Chris Laughman) on "Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems" was nominated for Best Energy Systems Paper Award; and, (3) A special session on Diversity, Equity, and Inclusion to improve recruitment and retention of underrepresented groups in STEM research.
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NEWS MERL Researchers Organize Two MiniSymposia and Five Invited Talks at the 2023 SIAM Conference on Optimization Date: May 31, 2023 - June 3, 2023
Where: 2023 SIAM Conference on Optimization
MERL Contacts: Devesh K. Jha; Arvind Raghunathan
Research Areas: Control, Optimization, RoboticsBrief- Arvind Raghunathan, Senior Team Leader and Senior Principal Research Scientist in Optimization & Intelligent Robotics team, will organize two minisymposia at the 2023 SIAM Conference on Optimization to be held in Seattle from May 31 to June 3. The two minisymposia titled "Optimization in Control – Algorithms, Applications, and Software" and "New Algorithmic Techniques for Global Optimization" will feature twelve invited speakers from academia and national labs.
Additionally, Arvind together with Devesh Jha, Principal Research Scientist in Optimization & Intelligent Robotics Team, and collaborators will present five invited talks covering the topics of algorithms for convex programs, multilinear programs, mixed-integer nonlinear programs, and robotics.
See:
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76268
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76269
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76270
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76256
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=75897
- Arvind Raghunathan, Senior Team Leader and Senior Principal Research Scientist in Optimization & Intelligent Robotics team, will organize two minisymposia at the 2023 SIAM Conference on Optimization to be held in Seattle from May 31 to June 3. The two minisymposia titled "Optimization in Control – Algorithms, Applications, and Software" and "New Algorithmic Techniques for Global Optimization" will feature twelve invited speakers from academia and national labs.
See All News & Events for Robotics -
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Internships
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CA2028: Mobile robots sensing, planning, and control
MERL is seeking a highly motivated intern to collaborate in the development and experimental validation of sensing, planning, and control methods in various robotic testbeds (quadrotors, turtlebots, and mini-cars) at MERL. The ideal candidate is enrolled in a Masters/PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in motion planning, control, optimization, computer vision, and their application in mobile robots, including experimental validation. The successful candidate is proficient in ROS, C/C++, and Python, and at least familiar with MATLAB. The expected duration of the internship is 6 months with a flexible start date in the late Fall/Winter 2023.
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CA1904: Numerical Optimal Control for Hybrid Dynamical Systems
MERL is looking for a highly motivated individual to work on tailored computational algorithms for numerical optimal control of hybrid dynamical systems and applications for decision making, motion planning and control of autonomous systems. The research will involve the study and development of numerical optimal control methods for systems with continuous dynamics and discrete logic, nonsmooth and/or switched dynamics, and the implementation and validation of such algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: mixed-integer programming (MIP), mathematical programs with complementarity constraints (MPCCs), modeling and formulation of optimal control problems for hybrid dynamical systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on MIPs, MPCCs or numerical optimal control, are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.
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CA1940: Autonomous vehicle planning and contro in uncertain environments
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.
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Recent Publications
- "A Lagrangian Inspired Polynomial Kernel for Robot Dynamics Identification", ICRA 2023 Workshop on Effective Representations, Abstractions, and Priors for Robot Learning (RAP4Robots), May 2023.BibTeX TR2023-063 PDF
- @inproceedings{Giacomuzzo2023may,
- author = {Giacomuzzo, Giulio and Dalla Libera, Alberto and Carli, Ruggero and Romeres, Diego},
- title = {A Lagrangian Inspired Polynomial Kernel for Robot Dynamics Identification},
- booktitle = {ICRA 2023 Workshop on Effective Representations, Abstractions, and Priors for Robot Learning (RAP4Robots)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-063}
- }
, - "Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control", European Control Conference (ECC), May 2023.BibTeX TR2023-065 PDF
- @inproceedings{Jha2023may,
- author = {Jha, Devesh K. and Jain, Siddarth and Romeres, Diego and Yerazunis, William S. and Nikovski, Daniel},
- title = {Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control},
- booktitle = {European Control Conference (ECC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-065}
- }
, - "Learning Control from Raw Position Measurements", American Control Conference (ACC), May 2023.BibTeX TR2023-060 PDF
- @inproceedings{Amadio2023may,
- author = {Amadio, Fabio and Dalla Libera, Alberto and Nikovski, Daniel N. and Carli, Ruggero and Romeres, Diego},
- title = {Learning Control from Raw Position Measurements},
- booktitle = {American Control Conference (ACC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-060}
- }
, - "Quadrotor Motion Planning in Stochastic Wind Fields", American Control Conference (ACC), May 2023.BibTeX TR2023-056 PDF
- @inproceedings{Greiff2023may,
- author = {Greiff, Marcus and Vinod, Abraham P. and Nabi, Saleh and Cairano, Stefano},
- title = {Quadrotor Motion Planning in Stochastic Wind Fields},
- booktitle = {American Control Conference (ACC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-056}
- }
, - "Learning Object Manipulation With Under-Actuated Impulse Generator Arrays", American Control Conference (ACC), May 2023.BibTeX TR2023-053 PDF
- @inproceedings{Kong2023may,
- author = {Kong, Chuizheng and Yerazunis, William S. and Nikovski, Daniel},
- title = {Learning Object Manipulation With Under-Actuated Impulse Generator Arrays},
- booktitle = {American Control Conference (ACC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-053}
- }
, - "Chance-Constrained Optimization in Contact-rich Systems", American Control Conference (ACC), May 2023.BibTeX TR2023-061 PDF
- @inproceedings{Shirai2023may4,
- author = {Shirai, Yuki and Jha, Devesh K. and Raghunathan, Arvind and Romeres, Diego},
- title = {Chance-Constrained Optimization in Contact-rich Systems},
- booktitle = {American Control Conference (ACC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-061}
- }
, - "MPC with Integrated Evasive Maneuvers for Failure-safe Automated Driving", American Control Conference (ACC), May 2023.BibTeX TR2023-055 PDF
- @inproceedings{Skibik2023may,
- author = {Skibik, Terrence and Vinod, Abraham P. and Weiss, Avishai and Di Cairano, Stefano},
- title = {MPC with Integrated Evasive Maneuvers for Failure-safe Automated Driving},
- booktitle = {American Control Conference (ACC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-055}
- }
, - "Integral Action NMPC for Tight Maneuvers of Articulated Vehicles", American Control Conference (ACC), May 2023.BibTeX TR2023-058 PDF
- @inproceedings{You2023may,
- author = {You, Sixiong and Greiff, Marcus and Quirynen, Rien and Ran, Shuangxuan and Wang, Yebin and Berntorp, Karl and Dai, Ran and Di Cairano, Stefano},
- title = {Integral Action NMPC for Tight Maneuvers of Articulated Vehicles},
- booktitle = {American Control Conference (ACC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-058}
- }
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- "A Lagrangian Inspired Polynomial Kernel for Robot Dynamics Identification", ICRA 2023 Workshop on Effective Representations, Abstractions, and Priors for Robot Learning (RAP4Robots), May 2023.
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Videos
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A Virtual Reality Teleoperation Interface for Industrial Robot Manipulators
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Simultaneous Tactile Estimation and Control of Extrinsic Contact
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Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization
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Tactile-Filter: Interactive Tactile Perception for Part Mating
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[MERL Seminar Series Spring 2023] Towards Complex Language in Partially Observed Environments
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Tactile tool manipulation
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Robot Locomotion by Automated Controller Tuning
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Real-time Mixed-integer Programming for Vehicle Decision Making and Motion Planning
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[MERL Seminar Series Spring 2022] Hybrid robotics and implicit learning
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[MERL Seminar Series Spring 2022] Exact Structural Analysis of Multimode Modelica Models
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[MERL Seminar Series Spring 2022] Self-Supervised Scene Representation Learning
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[MERL Seminar Series 2021] Learning to See by Moving: Self-supervising 3D scene representations for perception, control, and visual reasoning
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Robotic Research at MERL
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Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
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Modelica-Based Modeling and Control of a Delta Robot
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Towards Human-Level Learning of Complex Physical Puzzles
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Assembly of Belt Drive Units
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Examples of Robotic Manipulation
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Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
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Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving
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Deep Reactive Planning in Dynamic Environments
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Monte Carlo Probabilistic Inference for Learning Control
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Experimental Validation of Reachability-based Decision Making for Autonomous Driving
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Downloads