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.
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief
Date: October 10, 2019
- 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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Where: 3rd IAVSD Workshop on Dynamics of Road Vehicles: Connected and Automated Vehicles
MERL Contact: Stefano Di Cairano
Research Areas: Control, Optimization, Robotics, Signal Processing, Dynamical SystemsBrief
Date: April 28, 2019
- Stefano Di Cairano, Distinguished Scientist and Senior Team Leader in the Control and Dynamical Systems Group, will give an invited talk entitled: "Modularity, integration and synergy in architectures for autonomous driving" that covers recent work in the lab concerning building a modular, robust control framework for autonomous driving.
Where: CEATEC'18, Makuhari Messe, Tokyo
MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar; William Yerazunis
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, RoboticsBrief
Date: October 15, 2018 - October 19, 2018
- MERL's work on robot learning algorithms was demonstrated at CEATEC'18, Japan's largest IT and electronics exhibition and conference held annually at Makuhari Messe near Tokyo. A team of researchers from the Data Analytics Group at MERL and the Artificial Intelligence Department of the Information Technology Center (ITC) of MELCO presented an interactive demonstration of a model-based artificial intelligence algorithm that learns how to control equipment autonomously. The algorithm developed at MERL constructs models of mechanical equipment through repeated trial and error, and then learns control policies based on these models. The demonstration used a circular maze, where the objective is to drive a ball to the center of the maze by tipping and tilting the maze, a task that is difficult even for humans; approximately half of the CEATEC'18 visitors who tried to steer the ball by means of a joystick could not bring it to the center of the maze within one minute. In contrast, MERL's algorithm successfully learned how to drive the ball to the goal within ten seconds without the need for human programming. The demo was at the entrance of MELCO's booth at CEATEC'18, inviting visitors to learn more about MELCO's many other AI technologies on display, and was seen by an estimated more than 50,000 visitors over the five days of the expo.
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CD1382: Motion Planning in Dynamic Environment
MERL is seeking a highly skilled and self-motivated intern to work on motion planning of nonholonomic system in dynamic environments. The ideal candidate should have solid backgrounds in task allocation, scheduling, and motion planning under dynamic and stochastic environment. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, computer science, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.
CD1383: Collaborative Estimation for Robotic Manipulators
MERL is seeking a highly skilled and self-motivated intern to conduct research on condition monitoring for robotic manipulators. The ideal candidate should have solid backgrounds in robotic manipulators, stochastic estimation methods for dynamical systems, and collaborative strategies over multi-agents. Experience of applying machine learning to dynamical systems is a strong plus. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.
CV1375: Computer Vision for Robotic Manipulation
MERL is looking for a highly motivated intern to work on computer vision for robotic manipulation. There are several available topics to choose from including active perception, grasp detection, and intent recognition for human-robot interaction. The ideal candidate would be a Ph.D. student with a strong background in computer vision, deep learning, and/or robotics. Proficiency in Python programming is necessary and experience with ROS is a plus. Successful candidate will collaborate with MERL researchers to develop algorithms, conduct experiments, and prepare manuscripts for scientific publications. Start date is flexible and expected duration of the internship is at least 3 months. Interested candidates are encouraged to apply with their recent CV, list of related publications, and/or links to GitHub repositories (if any).
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- "Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2019. ,
- "Quasi-Newton Trust Region Policy Optimization", Conference on Robot Learning (CoRL), October 2019. ,
- "Detection, Tracking and 3D Modeling of Objects with Sparse RGB-D SLAM and Interactive Perception", IEEE-RAS International Conference on Humanoid Robots, October 2019. ,
- "Learning Heuristic Functions for Mobile Robot Path Planning Using Deep Neural Networks", International Conference on Automated Planning and Scheduling (ICAPS), July 2019. ,
- "Motion Planning of Autonomous Road Vehicles by Particle Filtering: Implementation and Validation", American Control Conference (ACC), July 2019. ,
- "Anomaly Detection for Insertion Tasks in Robotic Assembly Using Gaussian Process Models", European Control Conference (ECC), June 2019. ,
- "Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze", IEEE International Conference on Robotics and Automation (ICRA), May 2019. ,
- "Learning Tasks in a Complex Circular Maze Environment", Modeling the Physical World: Perception, Learning, and Control, NIPS Workshop, December 2018. ,