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Pulkit Agrawal
Pulkit Agrawal
Massachusetts Institute of Technology, USA
Talk title: TBA
Abstract:
Bio: Pulkit Agrawal is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. He earned his Ph.D. from UC Berkeley and his bachelor's degree from IIT Kanpur and was awarded the Director's Gold Medal. His work has received multiple Best Paper Awards, the IEEE Early Career Award in Robotics, the IROS Toshio Fukuda Young Professional Award, the IIT Kanpur Young Alumnus Award, the Sony Faculty Research Award, the Salesforce Research Award, the Amazon Research Award, the Signatures Fellow Award, the Fulbright Science and Technology Award, and others.
Benjamin Alt
Benjamin Alt
Talk title: Semantic Digital Twins and Virtual Robot Labs for Reproducible, Accessible Scientific Discovery
Abstract: Autonomous robots promise to accelerate scientific discovery across all disciplines of science. Using VR and semantic digital twin technologies, scientists can define experiments, program robots, and make their experiments and results available online to the scientific community. The AICOR Virtual Research building is an open-source, open-science platform for creating, hosting and accessing robotics experiments in a standardized manner, along with the data of the corresponding real-world experiments. It promises to increase the replicability and transparency of robot-based scientific inquiry, and makes robotic experimentation available to underserved communities across the globe.
Bio: Benjamin Alt is the Technical Director at the AICOR Institute for Artificial Intelligence and Co-Founder of a robotics startup building a general-purpose robot cognitive architecture. His research aims to endow robots with the abilities to competently act in the real world, while ensuring safe and human-compatible behavior. His research interests include neurosymbolic machine learning, semantic digital twins and human-robot interaction.
AICOR Institute for Artificial Intelligence, University of Bremen, Germany
Daniel Chan
Daniel Chan
Medra AI,
USA
Talk title: Physical AI, Agents, LLMs - Path Towards Autonomous Science
Abstract: Recent progress in AI for science has made it easier to generate hypotheses, design experiments, and reason over results, but a major bottleneck remains in execution. In biology, real experiments still depend on physical context that is rarely captured in software alone: instrument quirks, process observations, real-time sensory cues, tacit know-how, and failure recovery. Medra’s approach is to build Physical AI Scientists that connect scientific reasoning to modular laboratory execution, capture rich closed-loop process data, and continuously improve protocols through repeated operation in real labs. Medra frames this system around three core ingredients: modularity, measurement, and continuous learning.
In this talk, I will share Medra’s perspective on what it takes to move from narrow automation to continuous science. I will discuss why execution is not just a downstream implementation detail, but a core scientific bottleneck, and how richer logging of process data, observations, images, tactile sensing, and error handling changes what can be optimized. I will also describe why scale matters. Medra recently built a laboratory of 100 Physical AI Scientists where every run contributes to a larger learning system for experimental execution and scientific iteration.
Bio: Daniel Chan is a Member of Technical Staff at Medra, where he leads robotics on Medra Lab 1. Previously, he led deployments and applied AI. His work sits at the intersection of robotics, AI, and biology. Before Medra, Daniel worked on robotics systems at Hyundai New Horizons Studio and Amazon’s Grand Challenge Lab. He holds BS and MS degrees in Mechanical Engineering from Stanford University.
Riley Hickman
Talk title: VALIANT: Enabling Self-Learning Laboratories for Pharmaceutical Formulation Development
Abstract: Advances in machine learning and high-throughput experimentation have created new opportunities to accelerate pharmaceutical development, yet most systems remain fragmented, separating computational design from laboratory execution. Bridging this gap requires integrated platforms that connect AI-driven experiment planning with robotic workflows under real-world constraints.
In this talk, we present VALIANT, a modular, general-purpose platform developed at Intrepid Labs for pharmaceutical formulation development. VALIANT integrates AI-driven design with robotic orchestration, device control, and data management to enable closed-loop iteration across the full development lifecycle. It is designed to operate within stringent data security, reproducibility, and regulatory requirements while supporting a broad range of formulation modalities.
We describe how VALIANT is used in practice to coordinate heterogeneous laboratory systems and accelerate in vitro development timelines. We highlight results from real-world deployments, including formulations that have progressed from early experimentation through scale-up and toward clinical evaluation with industry partners.
Finally, we discuss the productionization of these capabilities into an integrated application layer that serves as a control center for formulation development, unifying experiment planning, execution, and analysis. These end-to-end systems are critical for translating advances in AI and robotics into scalable impact in pharmaceutical science.
Bio: Dr. Riley Hickman is Co-Founder and Senior Director of Engineering at Intrepid Labs, a company focused on accelerating the development of pharmaceutical formulations by integrating machine learning, robotics, and pharmaceutical science. He earned his PhD from the University of Toronto under Professor Alán Aspuru-Guzik, where his research focused on autonomous research systems for accelerating the discovery of advanced materials and functional molecules.
Prior to and through founding Intrepid, Riley has worked at the intersection of computer science, robotics, chemistry, and biology to build systems that close the loop between prediction and experiment. At Intrepid, he leads the development of VALIANT, a framework for accelerated pharmaceutical formulation development that combines machine learning, lab automation, robotics, and software into a unified platform. VALIANT is designed to operate within the stringent data security, regulatory, and reproducibility constraints of the pharmaceutical industry while enabling rapid, scalable experimentation. Through this work, Riley has helped translate AI-driven discovery systems into real-world impact, with formulations developed using VALIANT now scaled and advancing toward clinical evaluation with pharmaceutical partners.
Intrepid Labs Inc., Canada
Genki N Kanda
Genki N Kanda
Institute of Science Tokyo, Japan
Talk title: The Living Laboratory: Fully Automated Science Powered by AI Agents and Physical AI
Abstract: We aim to realize a “Living Laboratory” by integrating experimental robots, AI agents, and physical AI, where biological research proceeds autonomously and scientific papers are generated continuously. While current laboratory automation mainly focuses on experimental execution, planning, preparation, and recovery (“Care”) still rely heavily on humans. We address this gap by tightly linking AI agents with physical AI to take on Care. Our approach assigns Planning Care to AI agents that translate natural language procedures into executable robotic workflows, generate consumable layouts and programs, and manage loop- and branch-rich experiments such as cell passaging. Operation Care is handled by Physical AI through a vision–language–action system that autonomously performs implicit tasks, validates programs in simulation, detects errors, and provides corrective suggestions in real time. At the Institute of Science Tokyo, we have established a centralized robotic facility integrating multiple experimental robots with VLA-enabled systems. In this talk, we will present our current progress toward the Living Laboratory, including system integration, AI-driven planning, and VLA-based operation in real laboratory environments.
Bio: Dr. Genki N. Kanda, Ph.D., PMP, is a Professor at the Institute of Science Tokyo, Medical Research Laboratory, Department of Robotic Science. He received his Ph.D. in Science from Osaka University in 2016. Since first encountering the Maholo LabDroid in 2015, Dr. Kanda has led research and implementation of laboratory automation in life sciences and regenerative medicine. In 2019, he founded the Laboratory Automation Suppliers’ Association (LASA) in Japan and continues to serve as its chairman, fostering industry–academia collaboration and community building.
Rodrigo Moreno
Rodrigo Moreno
Talk title: Navigating Modular Reconfigurable Lab Automation: A Quick to Reconfigure Platform
Abstract:
Bio: Rodrigo is an Assistant Professor working with easy to reconfigure Lab automation for chemistry synthesis processes at the IT University of Copenhagen. Rodrigo joined ITU in 2020. He holds a PhD in Systems and Computer Engineering from Universidad Nacional de Colombia. He started working in lab automation encapsulating chemical unit operations as part of the BIG-MAP EU project and nowadays continues working on reconfigurable lab automation systems with focus in nanoparticle synthesis. His research interests include AI and machine learning applied to robotics, evolutionary robotics, modular robots and automation. Other projects he is working on include the EMERGE project, an easy to build modular robot system and the MOZART EU project for developing modular surface manipulation.
REAL Lab, IT University of Copenhagen, Denmark
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