Robots today can perceive, plan, learn, and control, but these capabilities are often developed as separate computational modules. My research asks a fundamental question: how can a robot actively acquire information, predict the physical consequences of its actions, and use those predictions to make safe decisions in real time? I seek to answer this question by updating robots’ understanding of the physical world through interaction. This perspective treats perception not as a passive process, but as an active component of decision-making: robots should determine not only what action to take, but also what information they need to acquire to make better decisions.
Humans do not simply execute motions; they operate in a closed loop, continuously predicting the consequences of their motor commands while interacting with the world to acquire new information. I believe complex robotic systems should operate according to the same principle. My research is built on two complementary foundations: predictive planning and control, which enables robots to anticipate and reason about interactions, and information-aware feedback control, which enables robots to actively build, refine, and exploit visual and physical representations of the world through interaction. I seek to unify information optimization and predictive planning and control to bridge the gap between big data, perception, planning, and feedback control. This moves beyond idealized models toward robust, adaptive robotic systems capable of operating reliably in the real world.
Enhanced Dexterity Maps (EDM): A New Map for Manipulator Capability Analysis.
Haowen Yao, Riddhiman Laha, Luis F.C. Figueredo, and Sami Haddadin
IEEE Robotics and Automation Letters (RA-L), December 2023
Predictive Multi-Agent based Planning and Landing Controller for Reactive Dual-Arm Manipulation. [PDF] [Code]
Riddhiman Laha*, Marvin Becker*, Jonathan Vorndamme*, Juraj Vrabel, Luis F.C. Figueredo, Matthias A. Müller, and Sami Haddadin
IEEE Transactions on Robotics (T-RO), December 2023
S*: On Safe and Time Efficient Robot Motion Planning. [PDF]
Riddhiman Laha, Wenxi Wu, Ruiai Sun, Nico Mansfeld, Luis F.C. Figueredo, and Sami Haddadin
IEEE International Conference on Robotics and Automation (ICRA 2023), London, United Kingdom
Coordinated Motion Generation and Object Placement: A Reactive Planning and Landing Approach. [PDF]
Riddhiman Laha*, Jonathan Vorndamme*, Luis F.C. Figueredo, Zheng Qu, Abdalla Swikir, Christoph Jähne, and Sami Haddadin
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Prague, Czech Republic
Point-to-point Path Planning based on User Guidance and Screw Linear Interpolation. [PDF]
Riddhiman Laha, Anjali Rao, Luis F. C. Figueredo, Qing Chang, Sami Haddadin, and Nilanjan Chakraborty
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC 2021), Virtual, Online
Reactive Cooperative Manipulation based on Set Primitives and Circular Fields. [PDF]
Riddhiman Laha, Luis F. C. Figueredo, Juraj Vrabel, Abdalla Swikir, and Sami Haddadin
IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China
Task-specific Motion Planning using User Guidance, Imitation and Self-Evaluation [PDF]
Riddhiman Laha and Nilanjan Chakraborty
RSS Workshop on Causal Imitation in Robotics, Carnegie Mellon University, June 2018