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
Mouhacine
Benosman
Yebin
Wang
Toshiaki
Koike-Akino
William S.
Yerazunis
Karl
Berntorp
Scott A.
Bortoff
Radu
Corcodel
Siddarth
Jain
Tim K.
Marks
Matthew E.
Brand
Bingnan
Wang
Ye
Wang
Avishai
Weiss
Jianlin
Guo
Jonathan
Le Roux
Hassan
Mansour
Marcel
Menner
Philip V.
Orlik
Rien
Quirynen
Koon Hoo
Teo
Anthony
Vetro
Pedro
Miraldo
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Awards
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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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AWARD MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019 Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief- MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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News & Events
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NEWS MERL work on 3D Printing in Orbit featured in IEEE Spectrum Date: June 3, 2022
Where: IEEE Spectrum
MERL Contacts: Avishai Weiss; William S. Yerazunis
Research Areas: Applied Physics, Communications, RoboticsBrief- MERL's research on on-orbit manufacturing was recently featured in an IEEE Spectrum article. The article, titled How Satellites Will 3D Print Their Own Antennas in Space gives an overview of MERL's efforts towards developing a system that construct spacecraft parts in their natural environment-- that is, in space. The technology, called OOM for On-Orbit Manufacturing, provides a way to manufacture not just antenna dishes, but general freeform sturctures on orbit and in a vacuum, using an solar-hardened resin based approach. This technology includes both a special high performance liquid resin, as well as a 3D freeform printer capable of building objects far larger than the as-launched satellite.
An important aspect of the special resin is that all components have extremely low vapor pressures and do not boil away even in a vacuum. When exposed to solar ultraviolet, the resin hardens by polymerization crosslinking, forming a tough, rigid solid in a few seconds of exposure. No separate UV source is needed, making the entire process very energy efficient. Additionally, the crosslinking resin is heat resistant, and is unaffected to at least 400 degrees C. The 3D printer needed to print the resin is unlike common liquid-resin SLA printers- there is no vat of liquid resin, instead a shielded nozzle delivers the liquid resin directly to where the resin is needed. The result is the ability to construct large and very large structures, not just parabolic dishes, but also solar panel supports and structural trusswork, while in orbit. The system could even construct parts that were unanticipated during mission design and launch.
MERL's On-Orbit Manufacturing Technology had previously been featured in a Mitsubishi Electric Corporation Press Release and was recently on display at a recent press exhibition in Tokyo, Japan.
IEEE Spectrum is the flagship magazine and website of the IEEE, the world’s largest professional organization devoted to engineering and the applied sciences. IEEE Spectrum has a circulation of over 400,000 engineers worldwide, making it one of the leading science and engineering magazines.
- MERL's research on on-orbit manufacturing was recently featured in an IEEE Spectrum article. The article, titled How Satellites Will 3D Print Their Own Antennas in Space gives an overview of MERL's efforts towards developing a system that construct spacecraft parts in their natural environment-- that is, in space. The technology, called OOM for On-Orbit Manufacturing, provides a way to manufacture not just antenna dishes, but general freeform sturctures on orbit and in a vacuum, using an solar-hardened resin based approach. This technology includes both a special high performance liquid resin, as well as a 3D freeform printer capable of building objects far larger than the as-launched satellite.
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NEWS MERL researchers presented 5 papers and an invited workshop talk at ICRA 2022 Date: May 23, 2022 - May 27, 2022
Where: International Conference on Robotics and Automation (ICRA)
MERL Contacts: Ankush Chakrabarty; Stefano Di Cairano; Siddarth Jain; Devesh K. Jha; Pedro Miraldo; Daniel N. Nikovski; Rien Quirynen; Arvind Raghunathan; Diego Romeres; Abraham P. Vinod; Yebin Wang
Research Areas: Artificial Intelligence, Machine Learning, RoboticsBrief- MERL researchers presented 5 papers at the IEEE International Conference on Robotics and Automation (ICRA) that was held in Philadelphia from May 23-27, 2022. The papers covered a broad range of topics from manipulation, tactile sensing, planning and multi-agent control. The invited talk was presented in the "Workshop on Collaborative Robots and Work of the Future" which covered some of the work done by MERL researchers on collaborative robotic assembly. The workshop was co-organized by MERL, Mitsubishi Electric Automation's North America Development Center (NADC), and MIT.
See All News & Events for Robotics -
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Internships
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CA1795: Path Planning and Control for Autonomous Articulated Vehicles
MERL is seeking a highly motivated and qualified intern to collaborate with multiple researchers on the implementation and experimental validation of algorithms for path/motion planning, optimal control and reference tracking in autonomous articulated vehicles. The ideal candidate has a background in either path planning or model predictive control (MPC) for autonomous (articulated) vehicles, and the candidate should be familiar with optimal control, vehicle dynamics, A* search, Matlab and Simulink, and C/C++ code generation. Any experience with dSPACE (e.g., MicroAutoBox or Scalexio) is a plus. MS or PhD students in control, robotics, electrical and mechanical, or related areas, are encouraged to apply. Start date for this internship is as soon as possible, and the expected duration is about 3-6 months.
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CV1703: Software development in ROS for robotic manipulation
MERL is offering an internship position for non-research software development for robotic manipulation. The scope of the internship is to develop robust ROS packages by refactoring existing experimental code. The position is open to prospective candidates with very strong programming skills in ROS (Robot Operating System) using C++ primarily and Python respectively. The selected intern will have a software engineering role rather than research oriented. The position is open to both senior undergraduate students and master students. Flexible start and end dates.
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CA1728: Safe data-driven control of dynamical systems under uncertainty
MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety and performance in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (robust and stochastic) model predictive control, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in submission of relevant results to peer-reviewed conference proceedings and journals, and development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is Summer 2022.
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Recent Publications
- , "Python-based Open Source Package for Optimization of Contact-rich Systems", Robotics: Science and Systems, June 2022.BibTeX TR2022-089 PDF
- @inproceedings{Raghunathan2022jun,
- author = {Raghunathan, Arvind and Jha, Devesh K. and Romeres, Diego},
- title = {Python-based Open Source Package for Optimization of Contact-rich Systems},
- booktitle = {Robotics: Science and Systems},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-089}
- }
- , "Robust Pivoting Manipulation Using Bilevel Contact-Implicit Optimization", RSS Workshop on The Science of Bumping into Things, June 2022.BibTeX TR2022-090 PDF
- @inproceedings{Shirai2022jun,
- author = {Shirai, Yuki and Jha, Devesh K. and Raghunathan, Arvind and Romeres, Diego},
- title = {Robust Pivoting Manipulation Using Bilevel Contact-Implicit Optimization},
- booktitle = {RSS Workshop on The Science of Bumping into Things},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-090}
- }
- , "Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots", IEEE Robotics and Automation Letters, DOI: 10.1109/LRA.2022.3185387, Vol. 7, No. 3, pp. 7802-7809, June 2022.BibTeX TR2022-085 PDF
- @article{Schperberg2022jun,
- author = {Schperberg, Alexander and Di Cairano, Stefano and Menner, Marcel},
- title = {Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots},
- journal = {IEEE Robotics and Automation Letters},
- year = 2022,
- volume = 7,
- number = 3,
- pages = {7802--7809},
- month = jun,
- doi = {10.1109/LRA.2022.3185387},
- url = {https://www.merl.com/publications/TR2022-085}
- }
- , "PYROBOCOP: Python-based Robotic Control & Optimization Package for Manipulation", IEEE International Conference on Robotics and Automation (ICRA), May 2022.BibTeX TR2022-057 PDF Software
- @inproceedings{Raghunathan2022may,
- author = {Raghunathan, Arvind and Jha, Devesh K. and Romeres, Diego},
- title = {PYROBOCOP: Python-based Robotic Control & Optimization Package for Manipulation},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-057}
- }
- , "Synthesizing and Simulating Volumetric Meshes from Vision-based Tactile Imprints", IEEE ICRA 2022 Workshop on Reinforcement Learning for Contact-Rich Manipulation, May 2022.BibTeX TR2022-058 PDF
- @inproceedings{Zhu2022may3,
- author = {Zhu, Xinghao and Jain, Siddarth and Tomizuka, Masayoshi and van Baar, Jeroen},
- title = {Synthesizing and Simulating Volumetric Meshes from Vision-based Tactile Imprints},
- booktitle = {IEEE ICRA 2022 Workshop on Reinforcement Learning for Contact-Rich Manipulation},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-058}
- }
- , "Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning", IEEE International Conference on Robotics and Automation (ICRA), DOI: 10.1109/ICRA46639.2022.9812309, May 2022, pp. 10890-10897.BibTeX TR2022-056 PDF
- @inproceedings{Leu2022may,
- author = {Leu, Jessica and Wang, Yebin and Tomizuka, Masayoshi and Di Cairano, Stefano and },
- title = {Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2022,
- pages = {10890--10897},
- month = may,
- doi = {10.1109/ICRA46639.2022.9812309},
- url = {https://www.merl.com/publications/TR2022-056}
- }
- , "Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints", IEEE International Conference on Robotics and Automation (ICRA), May 2022.BibTeX TR2022-055 PDF
- @inproceedings{Zhu2022may2,
- author = {Zhu, Xinghao and Jain, Siddarth and Tomizuka, Masayoshi and van Baar, Jeroen},
- title = {Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-055}
- }
- , "Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization", IEEE International Conference on Robotics and Automation (ICRA), May 2022.BibTeX TR2022-045 PDF
- @inproceedings{Shirai2022may,
- author = {Shirai, Yuki and Jha, Devesh K. and Raghunathan, Arvind and Romeres, Diego},
- title = {Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-045}
- }
- , "Python-based Open Source Package for Optimization of Contact-rich Systems", Robotics: Science and Systems, June 2022.
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Videos
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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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Software Downloads
